Neural Architecture Search (NAS) has emerged as a crucial method for automating the design of deep learning models. Despite its potential, NAS frequently requires substantial computational and hardware resources. To mitigate these challenges, transferable NAS (TNAS) has been introduced, leveraging prior NAS results to enhance performance on new tasks. However, existing methods largely focus on knowledge transfer within identical neural search spaces, overlooking the potential for cross-domain transferability. Motivated by this gap, we explore evolutionary TNAS across heterogeneous search spaces by learning common neural representations. In particular, we introduce a novel approach that encodes both operational and topological information of neural architectures into a unified sequence using a simple tokenizer. This sequence is then processed by a variational auto-encoder, with a Transformer-based encoder to capture rich neural representations and a decoder that reconstructs the original sequence. By utilizing these latent representations, we further establish an inter-domain mapping that acts as a bridge, enabling effective explicit solution transfer among diverse search spaces to enhance the evolutionary NAS process. To harness this capability, we develop an evolutionary sequential transfer optimization approach that transfers knowledge during population initialization, providing both flexibility and adaptability. To the best of our knowledge, this work serves as the first attempt in the literature exploring evolutionary TNAS across diverse spaces. Moreover, we demonstrate the utility of our method through comprehensive empirical studies using different architecture spaces, including NAS-Bench-101, NAS-Bench-201, and the DARTS search space. Our results show that the proposed method significantly enhances the adaptability and performance of NAS across varied domains.

Multi-objective optimization problems (MOPs) are ubiquitous in real-world applications, presenting a complex challenge of balancing multiple conflicting objectives. Traditional multi-objective evolutionary algorithms (MOEAs), though effective, often rely on domain-specific expertise for improved optimization performance, hindering adaptability to unseen MOPs. In recent years, the Large Language Models (LLMs) has revolutionized software engineering by enabling the autonomous generation and refinement of programs. Leveraging this breakthrough, we propose a new LLM-based framework that autonomously designs MOEAs for solving MOPs. The proposed framework includes a robust testing module to refine the generated MOEA through error-driven dialogue with LLMs, a dynamic selection strategy along with informative prompting-based crossover and mutation to fit textual optimization pipeline. Our approach facilitates the design of MOEA without the extensive demands for expert intervention, thereby speeding up the innovation of MOEA. Empirical studies across various MOP categories validate the robustness and superior performance of our proposed framework.

Transfer reinforcement learning (TRL) aims to boost the efficiency of reinforcement learning (RL) agents by leveraging knowledge from related tasks. Prior research primarily focuses on intradomain transfer, overlooking the complexities of transferring knowledge across tasks with differing state and action spaces. Recent efforts in cross-domain TRL aim to bridge this gap by establishing mappings between disparate source and target spaces, thereby enabling knowledge transfer across RL tasks with varied state and action configurations. However, existing studies often rely on strict prior assumptions about the relationships between state spaces, which limits their practical generality. In this article, we propose a novel approach to cross-domain TRL based on seeded graph matching, which enables alignment between source and target tasks regardless of differences in their state-action spaces. In particular, we model RL tasks as directed graphs, identify seed node pairs based on common RL properties, and devise a graph matching algorithm to align the source and target tasks by leveraging their structural characteristics. Building on this alignment, we introduce a policy-based transfer algorithm that improves the performance of the target RL task as its RL process progresses. Finally, we conduct comprehensive empirical studies on both discrete and continuous tasks with diverse state-action spaces. The experimental results validate the effectiveness of the proposed algorithm.

Transferable neural architecture search (TNAS) has been introduced to design efficient neural architectures for multiple tasks, to enhance the practical applicability of NAS in real-world scenarios. In TNAS, architectural knowledge accumulated in previous search processes is reused to warm up the architecture search for new tasks. However, existing TNAS methods still search in an extensive search space, necessitating the evaluation of numerous architectures. To overcome this challenge, this work proposes a novel transfer paradigm, i.e., design principle transfer. In this work, the linguistic description of various structural components' effects on architectural performance is termed design principles. They are learned from established architectures and then can be reused to reduce the search space tasks by discarding unpromising architectures. Searching in the refined search space can boost both the search performance and efficiency for new NAS tasks. To this end, a large language model (LLM)-assisted design principle transfer (LAPT) framework is devised. In LAPT, LLM is applied to automatically reason the design principles from a set of given architectures, and then a principle adaptation method is applied to refine these principles progressively based on the search results. Experimental results demonstrate that LAPT can beat the state-of-the-art TNAS methods on most tasks and achieve comparable performance on the remainder.

Evolutionary transfer optimization (ETO) has been gaining popularity in research over the years due to its outstanding knowledge transfer ability to address various challenges in optimization. However, a pressing issue in this field is that the invention of new ETO algorithms has far outpaced the development of fundamental theories needed to clearly understand the key factors contributing to the success of these algorithms for effective generalization. In response to this challenge, this study aims to establish theoretical foundations for analogy-based ETO, specifically to support various algorithms that frequently reference a key concept known as similarity. First, we introduce analogical reasoning and link its subprocesses to three key issues in ETO. Then, we develop theories for analogy-based knowledge transfer, rooted in the principles that underlie the subprocesses. Afterwards, we present two theorems related to the performance gain of analogy-based knowledge transfer, namely unconditionally nonnegative performance gain and conditionally positive performance gain, to theoretically demonstrate the effectiveness of various analogy-based ETO methods. Last but not least, we offer a novel insight into analogy-based ETO that interprets its conditional superiority over traditional evolutionary optimization through the lens of the no free lunch theorem for optimization.

Large language models (LLMs) have exhibited their problem-solving abilities in mathematical reasoning. Solving realistic optimization (OPT) problems in application scenarios requires advanced and applied mathematics ability. However, current OPT benchmarks that merely solve linear programming are far from complex realistic situations. In this work, we propose **OptiBench**, a benchmark for End-to-end optimization problem-solving with human-readable inputs and outputs. **OptiBench** contains rich optimization problems, including linear and nonlinear programming with or without tabular data, which can comprehensively evaluate LLMs' solving ability. In our benchmark, LLMs are required to call a code solver to provide precise numerical answers. Furthermore, to alleviate the data scarcity for optimization problems, and to bridge the gap between open-source LLMs on a small scale (e.g., Llama-3-8b) and closed-source LLMs (e.g., GPT-4), we further propose a data synthesis method namely ***ReSocratic***. Unlike general data synthesis methods that proceed from questions to answers, \ReSocratic first incrementally synthesizes formatted optimization demonstration with mathematical formulations step by step and then back-translates the generated demonstrations into questions. Based on this, we synthesize the ***ReSocratic-29k*** dataset. We further conduct supervised fine-tuning with ***ReSocratic-29k*** on multiple open-source models. Experimental results show that ***ReSocratic-29k*** significantly improves the performance of open-source models.

Algorithm selection aims to identify the optimal performing algorithm before execution. Existing techniques typically focus on the observed correlations between algorithm performance and meta-features. However, little research has explored the underlying mechanisms of algorithm selection, specifically what characteristics an algorithm must possess to effectively tackle problems with certain feature values. This gap not only limits the explainability but also makes existing models vulnerable to data bias and distribution shift. This paper introduces directed acyclic graph (DAG) to describe this mechanism, proposing a novel modeling paradigm that aligns more closely with the fundamental logic of algorithm selection. By leveraging DAG to characterize the algorithm feature distribution conditioned on problem features, our approach enhances robustness against marginal distribution changes and allows for finer-grained predictions through the reconstruction of optimal algorithm features, with the final decision relying on differences between reconstructed and rejected algorithm features. Furthermore, we demonstrate that, the learned DAG and the proposed counterfactual calculations offer our approach with both model-level and instance-level explainability.

Model merging is a technique that combines multiple large pretrained models into a single model, enhancing performance and broadening task adaptability without original data or additional training. However, most existing model merging methods focus primarily on exploring the parameter space, merging models with identical architectures. Despite its potential, merging in the architecture space remains in its early stages due to the vast search space and challenges related to layer compatibility. This paper designs a hierarchical model merging framework named HM3, formulating a bilevel multi-objective model merging problem across both parameter and architecture spaces. At the parameter level, HM3 integrates existing merging methods to quickly identify optimal parameters. Based on these, an actor-critic strategy with efficient policy discretization is employed at the architecture level to explore inference paths with Markov property in the layer-granularity search space for reconstructing these optimal models. By training reusable policy and value networks, HM3 learns Pareto optimal models to provide customized solutions for various tasks. Experimental results on language and vision tasks demonstrate that HM3 outperforms methods focusing solely on the parameter or architecture space.

Interactive graph search (IGS) has emerged as a powerful paradigm for information retrieval across diverse applications. The goal of IGS is to identify the most appropriate (i.e., deepest) node within a hierarchy for an unknown object, typically leveraging human intelligence such as crowdsourcing as the oracle. Existing IGS algorithms usually rely on reachability queries, such as "is the target node reachable from node x ?", and assume that correct answers are always available. However, in practice, answering such queries is challenging due to the requirement for domain-specific knowledge, resulting in frequent errors in the oracle's responses. As a consequence, the reachability-query-based approaches would perform poorly. In this paper, we propose a practical solution to the IGS problem, leveraging the power of large language models (LLMs) to tackle the issue of reachability queries. Specifically, we formally analyze the inherent properties of real-world hierarchies with the notion of ambiguous nodes and overlapping nodes to debunk the difficulty of reachability queries. In addition, we develop a practical oracle based on LLMs that can answer reachability queries on (near) leaf nodes accurately. Building on the LLM oracle, we propose a similarity-based upward search algorithm, namely SuS, to address the IGS problem. We further enhance SuS with layer-wise search and fast initialization techniques. We evaluate SuS on two real-world datasets against four baseline methods, and the experimental results clearly demonstrate the superiority of our solution.

Evolutionary multitasking (EMT) is an emerging approach for solving multitask optimization problems (MTOPs) and has garnered considerable research interest. The implicit EMT is a significant research branch that utilizes evolution operators to enable knowledge transfer (KT) between tasks. However, current approaches in implicit EMT face challenges in adaptability, due to the limited use of different evolution operators with different parameter settings and insufficient utilization of evolutionary states for performing KT. This results in suboptimal exploitation of implicit KT's potential to tackle a variety of MTOPs. To overcome these limitations, we propose a novel learning-to-transfer (L2T) framework to automatically discover efficient KT policies for the MTOPs at hand. Our framework conceptualizes the KT process as a learning agent's sequence of strategic decisions within the EMT process. We propose an action formulation for deciding when and how to transfer, a state representation with informative features of evolution states, a reward formulation concerning convergence and transfer efficiency gain, and the environment for the agent to interact with MTOPs. We employ an actor-critic network structure for the agent and learn the policy via proximal policy optimization. This learned agent can be integrated with various evolutionary algorithms, enhancing their ability to address unseen MTOPs. Comprehensive empirical studies on both synthetic and real-world MTOPs, encompassing diverse intertask relationships, function classes, and task distributions are conducted to validate the proposed L2T framework. The results show a marked improvement in the adaptability and performance of implicit EMT when solving a wide spectrum of unseen MTOPs.

Despite the increasing interest in sequential transfer optimization (STO), a comprehensive benchmark suite for systematically comparing various STO algorithms remains underexplored. Existing test problems, which are often manually configured and lack scalability, can result in biased and nongeneralizable algorithm performance. In light of the above, we first introduce four concepts for characterizing STO problems (STOPs) in this study and present an important feature, namely similarity distribution, to quantitatively delineate the relationship between the optimal solutions of source and target tasks. Subsequently, we present general design guidelines for STOPs and introduce a problem generator that demonstrates strong scalability. Specifically, the similarity distribution of a problem can be easily customized through a novel inverse generation strategy, allowing for a continuous spectrum that captures the diverse similarity relationships present in real-world scenarios. Lastly, a benchmark suite comprising 12 STOPs, characterized by a range of customized similarity relationships, has been developed using the proposed generator and will serve as a platform for examining various STO algorithms. For instance, biased transferability representation, irregular mapping learning behaviors, and performance improvements unrelated to search experience are significant empirical findings that previous benchmarks failed to reveal, yet can be effectively identified through our test problems. The source code of the proposed problem generator is available at https://github.com/XmingHsueh/STOP-G.

Evolutionary multitasking (EMT) is an emerging research direction within the evolutionary computation community, attempting to concurrently solve multiple optimization tasks by exploiting the underlying synergies between the tasks. Recently, numerous explicit transfer strategies have been developed for enhancing positive transfer among optimization tasks. Nevertheless, most of these methods conduct knowledge transfer by transferring the best solutions from a source task to the target task, while ignoring the proper use of information from the target task in solution selection. As a result, the transferred solutions could not well adapt to the target task, thus limiting the effectiveness of knowledge transfer across tasks. To address this issue, this paper proposes a solution selection method based on the lower confidence bound (LCB) for EMT, which is designed by leveraging task-specific information of both source and target tasks. With the proposed LCB metric, a number of high-quality solutions that could be more helpful for the target task can be selected and transferred to enhance positive transfer in EMT. To verify the effectiveness of the proposed approach, the solution selection method is embedded into several existing EMT algorithms and then evaluated on the single-objective multitasking benchmarks, the multiobjective multitasking benchmark, and a real-world application. The obtained results confirmed the generality and efficacy of the proposed solution selection approach.

Large language models (LLMs) have not only revolutionized natural language processing but also extended their prowess to various domains, marking a significant stride toward artificial general intelligence. The interplay between LLMs and evolutionary algorithms (EAs), despite differing in objectives and methodologies, share a common pursuit of applicability in complex problems. Meanwhile, EA can provide an optimization framework for LLM’s further enhancement under closed box settings, empowering LLM with flexible global search capacities. On the other hand, the abundant domain knowledge inherent in LLMs could enable EA to conduct more intelligent searches. Furthermore, the text processing and generative capabilities of LLMs would aid in deploying EAs across a wide range of tasks. Based on these complementary advantages, this article provides a thorough review and a forward-looking roadmap, categorizing the reciprocal inspiration into two main avenues: 1) LLM-enhanced EA and 2) EA-enhanced LLM. Some integrated synergy methods are further introduced to exemplify the complementarity between LLMs and EAs in diverse scenarios, including code generation, software engineering, neural architecture search, and various generation tasks. As the first comprehensive review focused on the EA research in the era of LLMs, this article provides a foundational stepping stone for understanding the collaborative potential of LLMs and EAs. The identified challenges and future directions offer guidance for researchers and practitioners to unlock the full potential of this innovative collaboration in propelling advancements in optimization and artificial intelligence. We have created a GitHub repository to index the relevant papers:https://github.com/wuxingyu-ai/LLM4EC.

Expensive optimization problems (EOPs) have attracted increasing research attention over the decades due to their ubiquity in a variety of practical applications. Despite many sophisticated surrogate-assisted evolutionary algorithms (SAEAs) that have been developed for solving such problems, most of them lack the ability to transfer knowledge from previously-solved tasks and always start their search from scratch, making them troubled by the notorious cold-start issue. A few preliminary studies that integrate transfer learning into SAEAs still face some issues, such as defective similarity quantification that is prone to underestimate promising knowledge, surrogate-dependency that makes the transfer methods not coherent with the state-of-the-art in SAEAs, etc. In light of the above, a plug and play competitive knowledge transfer (CKT) method is proposed to boost various SAEAs in this article. Specifically, both the optimized solutions from the source tasks and the promising solutions acquired by the target surrogate are treated as task-solving knowledge, enabling them to compete with each other to elect the winner for expensive evaluation, thus boosting the search speed on the target task. Moreover, the lower bound of the convergence gain brought by the knowledge competition is mathematically analyzed, which is expected to strengthen the theoretical foundation of sequential transfer optimization. Experimental studies conducted on a series of benchmark problems and a practical application from the petroleum industry verify the efficacy of the proposed method. The source code of the CKT is available athttps://github.com/XmingHsueh/SAS-CKT.

Genetic programming (GP) is a widely recognized and powerful approach for symbolic regression (SR) problems. However, existing GP methods rely on a single form to solve the problem, which limits their search diversity and increases the likelihood of getting stuck in local optima, especially in complex scenarios. In this paper, we propose a general multiform GP framework to improve the performance of GP on complicated SR problems. As far as we know, this paper is the first attempt to integrate the multiform optimization paradigm with GP to accelerate the search performance. The key idea of the proposed framework is to construct multiple forms to solve the same problem cooperatively at the same time. During the evolution process, knowledge gained from different forms is shared among the solvers to improve the search diversity and efficiency. A knowledge transfer mechanism is specifically designed to facilitate knowledge transfer among GP solvers with different modeling forms. In addition, an adaptive resource control mechanism is designed to reallocate computing resources according to the problem-solving efficiency of different solvers to further improve search efficiency. To demonstrate the effectiveness of the proposed framework, a multiform GEP algorithm (MF-GEP) is designed and tested on 20 problems, including physical datasets, synthetic datasets, and real-world datasets. The experimental results have demonstrated the effectiveness of the proposed framework.

2D-3D cross modal retrieval (CMR) aims to retrieve query image matching points from a 3D reference map. Existing classical CMR datasets and methods commonly support database-based retrieval only, i.e., the point cloud retrieval results are fixed-scale geometric surfaces. The failure to consider geometric regions and information scales fundamentally limits the practical deployment of CMR in engineering systems that require dynamic spatial reasoning, such as autonomous navigation or three-dimensional industrial measurement. In this article, we introduce a new benchmark called cross modal regional retrieval, which extends the classic CMR to allow the free retrieval of associated regions within the point cloud from images. Toward this, a multiview training paradigm is proposed in the training phase, which enables the model to identify occluded points in the region based on a single view. Autoencoders are utilized to learn the mapping of fusion features from a single view to multiple views. We also convert the image retrieval task within the scene cloud into a point classification task in the image to implement global free retrieval. The information fusion and guidance provided by the global point cloud enhances the capability of image cross-modal retrieval. To match the input patterns of the model, we propose a method for constructing datasets from three benchmark sources. Extensive experiments demonstrate that our method achieves state-of-the-art performance compared to existing methods for 2D-3D cross modal regional retrieval.

Most state-of-the-art convolutional neural networks (CNNs) are characterized by excessive parameterization, leading to a high computational burden. Tensor decomposition has emerged as a model reduction technique for compressing deep neural networks. Previous approaches have predominantly relied on either Tucker decomposition or Canonical Polyadic (CP) decomposition for CNNs. However, CP decomposition exhibits exceptional compression capabilities in comparison to Tucker decomposition, which results in a more pronounced accuracy loss. This article introduces an efficient model compression method, termed TEC-CNN, designed to achieve significant compression while preserving accuracy levels comparable to those of the original models. In TEC-CNN, convolutional layers are identified to obtain convolutional kernels by analyzing given models under the principles of low-rank tensor decomposition, and then, calculating the ranks of convolutional kernels. Furthermore, an efficient decomposition schema for the convolutional kernel is proposed with approximate kernel tensor for reducing parameters. Additionally, a novel format of a convolutional sequence is presented and constructed with a reduced number of parameters to replace the original convolutional layers. Finally, the effectiveness of TEC-CNN is assessed across a range of computer vision tasks. For instance, in CIFAR-100 classification, ResNet18 is compressed to 4.1 MB, while Unext, when applied to image segmentation using the International Skin Imaging Collaboration (ISIC) dataset, is reduced to 3.419 MB. When employed for fire object detection with Yolov7, TEC-CNN achieves a model size reduction of 71.6 MB. Comprehensive experimental results underscore that our approach achieves significant model compression while preserving model performance.

Evolutionary transfer optimization (ETO), which combines evolutionary algorithms with knowledge transfer across related tasks to enhance search performance, has gained widespread attention from researchers in recent years. Multiform transfer optimization (MFTO) stands out as a representative transfer paradigm of ETO, aiming to exploit alternative formulations of the target task of interest. By leveraging useful knowledge acquired from alternative formulations to assist in solving the target task, MFTO has proven effective in tackling complex optimization problems, contributing to the growth of MFTO research. This paper provides a review of existing research progress in MFTO. Firstly, we introduce the fundamental aspects of MFTO, including the general framework and core components. Subsequently, we summarize the advances in MFTO from the perspectives of problems to be solved and the way of constructing alternative formulations. Lastly, we discuss promising future research directions. It is hoped that this survey can provide a thorough understanding of the MFTO framework and facilitate the development of more advanced MFTO algorithms and applications.

In cases of frequent problem-solving of multiobjective optimization tasks from a domain due to changing conditions or problem features, a growing number of individual tasks will be solved and stored in a database, providing an opportunity for a target task at hand to achieve better optimization performance through knowledge transfer from the previously-solved tasks, which is also known as sequential transfer optimization. Despite a variety of transfer algorithms that have been developed over the years, the research on the design of benchmark problems for evaluating such algorithms received far less attention. Oftentimes, the source and target tasks in existing test problems are manually assembled or extended from specific practical problems, limiting their ability to represent the diverse yet complex source-target similarity relationships in real-world problems. In light of this, we propose design methods to generate multiobjective sequential transfer optimization problems (MSTOPs) systematically in this work, wherein the Pareto manifolds of individual tasks and the manifold-based similarity between the tasks can be customized with ease, enabling a broad spectrum of representation of the diverse similarity relationships between the source-target Pareto manifolds of MSTOPs. Lastly, a benchmark suite with 12 test problems is developed using the proposed methods, which would serve as an arena for electing superior multiobjective sequential transfer optimization algorithms. The source code is available at https://github.com/XmingHsueh/MSTOP.

Advising is an effective method to enhance agent learning performance in multi-agent deep reinforcement learning. Existing advising methods typically rely on a teacher-student framework where a teacher agent provides student agents with action or Q-value advice. However, they share a common limitation: the advice from a teacher agent can only assist a student in making a one-time decision in the current state and cannot be internalized into the student agent’s knowledge to intrinsically change the student agent’s decision model. Consequently, the advice acts more like a one-time instruction from the teacher rather than a learning aid. If the student agent encounters the same problem again, it may still be unable to make a sound decision and need to request advice. This not only fails to rapidly enhance the agent’s decision-making ability fundamentally but also leads to a considerable waste of communication costs. Hence, we propose a multi-agent advice distillation framework through attention that allows the student agent to request advice from the experienced teacher and distill that advice into their own decision model via the self-attention mechanism. As a result, advice is fully utilized, allowing for a rapid and intrinsic improvement in the agent’s decision-making capabilities. Our empirical evaluations demonstrate that, compared to existing advising methods, our method significantly improves learning performance while reducing the communication cost.

Dynamic environments pose great challenges for expensive optimization problems, as the objective functions of these problems change over time and thus require remarkable computational resources to track the optimal solutions. Although data-driven evolutionary optimization and Bayesian optimization (BO) approaches have shown promise in solving expensive optimization problems in static environments, the attempts to develop such approaches in dynamic environments remain rarely explored. In this article, we propose a simple yet effective meta-learning-based optimization framework for solving the expensive dynamic optimization problems. This framework is flexible, allowing any off-the-shelf continuously differentiable surrogate model to be used in a plug-in manner, either in data-driven evolutionary optimization or BO approaches. In particular, the framework consists of two unique components: 1) the meta-learning component, in which a gradient-based meta-learning approach is adopted to learn experience (effective model parameters) across different dynamics along the optimization process and 2) the adaptation component, where the learned experience (model parameters) is used as the initial parameters for fast adaptation in the dynamic environment based on few shot samples. By doing so, the optimization process is able to quickly initiate the search in a new environment within a strictly restricted computational budget. Experiments demonstrate the effectiveness of the proposed algorithm framework compared to several state-of-the-art algorithms on common benchmark test problems under different dynamic characteristics.

Presents corrections to the paper, (Corrections to "Toward Adaptive Knowledge Transfer in Multifactorial Evolutionary Computation").

Many practical optimization problems in the fields of transportation, business, engineering, environmental economics, etc., involve more than one level of decision-making and can be modeled as a bi-level optimization problem with a nested structure of decision variables. Existing studies have made remarkable progress on bi-level single-objective problems. However, due to the increased complexities in terms of computation and decision-making, few efforts have been devoted to bi-level multi-objective optimization problems (BLMOPs). This paper proposes an evolutionary multi-form optimization paradigm that explores alternative formulations of the target task to assist in the search with the original formulation, namely BLMFO, for bi-level multi-objective optimization. Firstly, in the proposed framework, alternative formulations of the original problem are derived to facilitate the problem-solving and also alleviate computational overheads. Then, BLMFO performs the evolutionary search in the original problem space and the auxiliary task space simultaneously to combine searching for feasible solutions and exploring regions of promising solutions, thus ensuring the effectiveness of the proposed framework. Further, useful information is transferred across the original and auxiliary tasks via explicit knowledge transfer to enable complementary exploration for better optimization performance. To the best of our knowledge, this work serves as the first attempt to solve BLMOPs via multi-form evolutionary optimization in the literature. The framework is verified using four instantiation groups with different underlying baseline solvers on various benchmarks and practical problems. The experimental results show the effectiveness and superiority of the proposed framework in terms of performance indicators and the quality of final optimized solutions.

Evolutionary multitasking (EMT) has attracted much attention in the community of evolutionary computation recently. It intends to improve the performance of evolutionary optimization on multiple problems via knowledge learning and transfer across them while the optimization processes progress online. Existing EMT paradigms can be classified as explicit EMT (EEMT) and implicit EMT (IEMT) according to the mechanisms adopted in the knowledge transfer. With additional knowledge learning and transfer modules, the EEMT often brings flexible algorithmic designs and effective knowledge transfer against the IEMT. However, most of the existing EEMT studies are designed for continuous optimization problems. Due to the difficulty of learning problem-specific mappings across combinatorial optimization problems, EEMT for combinatorial optimization is still in the nascent stage. Furthermore, it is worth noting that, with the growing number of tasks in today’s real-world applications and the enlarged number of decision variables in each of the tasks, learning mappings across tasks becomes more challenging. Keeping the above in mind, this paper presents a novel EEMT algorithm with centralized learning for solving the large-scale and multi-objective combinatorial optimization in many-task manner, in which knowledge transfer across tasks is conducted based on a centralized learning model, instead of task-specific mappings which are required in existing EEMT studies. To investigate the performance of the proposed centralized learning assisted EEMT, comprehensive empirical studies have been conducted on the large-scale and multi-objective knapsack problems. Lastly, the efficacy of our proposed method is further validated on a real-world combinatorial optimization application.

Recently, a number of domain adaptation (DA) methods have been proposed for knowledge transfer in evolutionary multitasking (EMT). However, the learned mappings in these methods often have unique biases in representing the connection between source and target tasks. Few studies have paid attention to the complementarity of different mappings in knowledge transfer. To fill this research gap, this article proposes an ensemble method to combine multiple DA methods for knowledge transfer in EMT by considering the efficacy and diversity of these methods. First, a hierarchical clustering method is used to divide the population of each task into multiple clusters. Then, when two parental solutions are selected for knowledge transfer across tasks, the solutions within the same cluster are checked. In particular, if none of these solutions has been transferred before, the efficacy of DA methods is considered first by using roulette wheel selection based on the corresponding performance improvements in the evolutionary optimization process. Otherwise, the diversity of DA methods is emphasized by randomly selecting one of the DA methods for knowledge transfer. The effectiveness of our proposed ensemble method is validated by embedding it into existing state-of-the-art EMT algorithms, and the experimental results show that our algorithm outperforms several recently proposed EMT algorithms on most cases of two multitasking benchmark suites and one practical case.

Knowledge transfer from optimized problems has emerged as a promising technique for enhancing evolutionary search. However, most studies in this domain primarily concentrate on devising knowledge transfer mechanisms for specific problem domains, often lacking the examination of the fundamental aspects of knowledge transfer, i.e., what, when and how to transfer across diverse scenarios. This not only restricts the generality of these algorithms but also hinders their practical applicability. In light of this, this paper: 1) reviews a vast array of techniques associated with the crucial aspects of solution transfer and 2) conducts a series of experiments to explore the underlying transfer mechanisms that enhance the evolutionary search. In particular, we first define solution transferability in the context of evolutionary search, which provides a new perspective in understanding what, when and how to transfer in enhancing evolutionary search. Next, through comprehensive experiments, we find that the approximation and evaluation of solution transferability is of great importance in designing what, when and how to transfer towards enhanced evolutionary search. Furthermore, our empirical study also discusses the counterintuitive performance improvements unrelated to the search experience of source tasks. The source code for reproducing our experiments is available at https://github.com/XmingHsueh/STO-EC.

Evolutionary neural architecture search (ENAS) methods have been successfully used to design convolutional neural network (CNN) architectures automatically. These methods have achieved excellent performance in creating a specific neural architecture for a single task but are less efficient for multiple tasks. Existing ENAS frameworks always repeatedly perform the search from scratch for each task, even though these tasks may be solved by similar CNN architectures. This work presents an evolutionary multi-task convolutional neural architecture search (MTNAS) framework to enable efficient architecture searches in multi-task scenarios by incorporating architectural similarities. The proposed MTNAS constructs architectures for different tasks simultaneously by implementing a knowledge-sharing mechanism among multiple search processes. Specifically, promising architectures found in one search process can be transferred and reused to generate high-quality architectures for others. Furthermore, we devise an adaptive strategy to dynamically adjust the frequency of knowledge transfer, aiming to alleviate the potential effect of negative transfer. Extensive experiments demonstrate that MTNAS can outperform state-of-the-art NAS methods or achieve comparable performance in different tasks but with 2× less search cost.

Deep reinforcement learning has attracted wide interest because of its extraordinary capabilities in multiple fields. However, morphology optimization by using evolutionary computation techniques has not been intensively investigated. In this paper, we explore the use of genetic algorithms (GA) to automatically design the morphology of an agent. Evaluating the performance of an agent is very time-consuming because it needs to be trained from scratch. Moreover, it is computationally infeasible to train separate controllers for all possible different morphologies of agents to identify the optimal ones and is difficult to obtain the accurate cumulative reward of an agent to estimate the performance of the morphologies. To address these issues, we use a morphology comparator as a surrogate model to estimate the probability of one morphology being better than the other, instead of directly predicting the performance of each morphology. A set of surrogate models based on a radial basis function network are developed before evolution to make full use of the data to guide the search. Experimental results indicate that the proposed method is able to efficiently find out optimal morphologies to achieve better performance than the default morphology.

Prompt-based learning improves the performance of Pre-trained Language Models (PLMs) over few-shot learning and is suitable for low-resourced scenarios. However, it is challenging to deploy large PLMs online. Knowledge Distillation (KD) can compress large PLMs into small ones; yet, few-shot KD for prompt-tuned PLMs is challenging due to the lack of training data and the capacity gap between teacher and student models. We propose Boost-Distiller, the first few-shot KD algorithm for prompt-tuned PLMs with the help of the out-of-domain data. Apart from distilling the model logits, Boost-Distiller specifically considers heuristically-generated fake logits that improve the generalization abilities of student models. We further leverage the cross-domain model logits, weighted with domain expertise scores that measure the transferablity of out-of-domain instances. Experiments over various datasets show Boost-Distiller consistently outperforms baselines by a large margin.

Prompt-based learning has improved the few-shot learning performance of large-scale Pre-trained Language Models (PLMs). Yet, it is challenging to deploy large-scale PLMs in resource-constrained environments for online applications. Knowledge Distillation (KD) is a promising approach for PLM compression. However, distilling prompt-tuned PLMs in the few-shot learning setting is a non-trivial problem due to the lack of task-specific training data and KD techniques for the new prompting paradigm. We propose Prompt-Distiller, the first few-shot KD algorithm for prompt-tuned PLMs, which forces the student model to learn from both its pre-trained and prompt-tuned teacher models to alleviate the model overfitting problem. We further design a contrastive learning technique to learn higher-order dependencies from intermediate-layer representations of teacher models, considering different knowledge capacities of teacher and student models. Extensive experiments over various datasets show that Prompt-Distiller consistently outperforms baselines by a large margin.

Neural architecture search (NAS) has been a popular research topic for designing deep neural networks (DNNs) automatically. It is able to improve the design efficiency of neural architectures significantly for given learning tasks. Recently, instead of conducting architecture search in the original neural architecture space, many NAS approaches have been proposed to learn continuous representations from neural architectures for architecture search or estimation. In particular, Neural Architecture Optimization (NAO) is a representative method which encodes neural architectures as continuous representations by an auto-encoder and then performs continuous optimization in the encoded space with gradient-based methods. However, as NAO only considers the top-ranked architectures in learning the continuous representation, it could fail to construct a satisfied continuous optimization space which contains the expected high-quality neural architectures. Taking this cue, in this paper we propose a two-stage NAO (TNAO) to learn a more completed continuous representation of neural architectures which could provide a better optimization space for NAS. Specifically, by designing a pipeline that separates the training and search stages, we first build the training set via random sampling from the entire neural architecture search space, which is with the aim of collecting the well-distributed neural architectures for training. Moreover, to exploit the architectural semantic information with limited data effectively, we propose an improved Transformer auto-encoder for learning the continuous representation, which is supervised by ranking information of the neural architecture performance. Lastly, towards more effective optimization of neural architectures, we adopt a population-based swarm intelligence algorithm, i.e. competitive swarm optimization (CSO), with a newly designed remapping scoring scheme. To evaluate the efficiency of the proposed TNAO, comprehensive experimental studies are conducted on two common search spaces, i.e., NAS-Bench-101 and NAS-Bench-201. The architecture with the top 0.02% performance is discovered on NAS-Bench-101 and the best architecture in the CIFAR-10 dataset is obtained on NAS-Bench-201.

Evolutionary transfer optimization (ETO) has been becoming a hot research topic in the field of evolutionary computation, which is based on the fact that knowledge learning and transfer across the related optimization exercises can improve the efficiency of others. However, rare studies employ ETO to solve large-scale multiobjective optimization problems (LMOPs). To fill this research gap, this article proposes a new multitasking ETO algorithm via a powerful transfer learning model to simultaneously solve multiple LMOPs. In particular, inspired by adversarial domain adaptation in transfer learning, a discriminative reconstruction network (DRN) model (containing an encoder, a decoder, and a classifier) is created for each LMOP. At each generation, the DRN is trained by the currently obtained nondominated solutions for all LMOPs via backpropagation with gradient descent. With this well-trained DRN model, the proposed algorithm can transfer the solutions of source LMOPs directly to the target LMOP for assisting its optimization, can evaluate the correlation between the source and target LMOPs to control the transfer of solutions, and can learn a dimensional-reduced Pareto-optimal subspace of the target LMOP to improve the efficiency of transfer optimization in the large-scale search space. Moreover, we propose a real-world multitasking LMOP suite to simulate the training of deep neural networks (DNNs) on multiple different classification tasks. Finally, the effectiveness of the proposed algorithm has been validated in this real-world problem suite and the other two synthetic problem suites.

Neural architecture search (NAS) has attracted much attention in recent years. It automates the neural network construction for different tasks, which is traditionally addressed manually. In the literature, evolutionary optimization (EO) has been proposed for NAS due to its strong global search capability. However, despite the success enjoyed by EO, it is worth noting that existing EO algorithms for NAS are often very computationally expensive, which makes these algorithms unpractical in reality. Keeping this in mind, in this article, we propose an efficient memetic algorithm (MA) for automated convolutional neural network (CNN) architecture search. In contrast to existing EO algorithms for CNN architecture design, a new cell-based architecture search space, and new global and local search operators are proposed for CNN architecture search. To further improve the efficiency of our proposed algorithm, we develop a one-epoch-based performance estimation strategy without any pretrained models to evaluate each found architecture on the training datasets. To investigate the performance of the proposed method, comprehensive empirical studies are conducted against 34 state-of-the-art peer algorithms, including manual algorithms, reinforcement learning (RL) algorithms, gradient-based algorithms, and evolutionary algorithms (EAs), on widely used CIFAR10 and CIFAR100 datasets. The obtained results confirmed the efficacy of the proposed approach for automated CNN architecture design.

Vehicle routing problems (VRPs) are essential in logistics. In the literature, many exact and heuristic optimization algorithms have been proposed to solve the VRPs. These traditional approaches, however, generally start the optimization from scratch and ignore the experiences of solving related VRPs, which may lead to unnecessary computational costs in searching repeated problems and reduce the efficiency of vehicle routing. Recently, transfer optimization (TO) has been presented to speed up vehicle routing by reusing the knowledge learned from similarly solved VRPs. However, existing TO methods build connections across VRPs in a low-dimensional Euclidean space, which has limited modeling ability in the cases of having nonlinear correlations. Keeping this in mind, this article presents a study of TO equipped with the kernel method for fast vehicle routing. In contrast to existing TO methods, in this work, the learning of connections across VRPs for knowledge transfer is conducted in a reproducing kernel Hilbert space (RKHS), which thus has greater modeling capacity in nonlinear customer relationships between VPRs. To evaluate the performance of the proposed method, comprehensive empirical studies have been conducted using well-known VRP benchmarks, against existing state-of-the-art TO methods for vehicle routing. Finally, a well-known real-world VRP application given by a routing company (Jingdong), namely, the package delivery problem (PDP), is investigated to further assess the efficacy of our proposed method.

Large-scale multiobjective optimization problems (LSMOPs) refer to optimization problems with multiple con-flicting optimization objectives and hundreds or even thousands of decision variables. A key point in solving LSMOPs is how to balance exploration and exploitation so that the algorithm can search in a huge decision space efficiently. Large-scale multi-objective evolutionary algorithms consider the balance between exploration and exploitation from the individual's perspective. However, these algorithms ignore the significance of tackling this issue from the perspective of decision variables, which makes the algorithm lack the ability to search from different dimensions and limits the performance of the algorithm. In this paper, we propose a large-scale multiobjective optimization algorithm based on the attention mechanism, called (LMOAM). The attention mechanism will assign a unique weight to each decision variable, and LMOAM will use this weight to strike a balance between exploration and exploitation from the decision variable level. Nine different sets of LSMOP benchmarks are conducted to verify the algorithm proposed in this paper, and the experimental results validate the effectiveness of our design.

In this paper, we study the problem of constrained path search with submodular function maximization (CPS-SM). We aim to find the path with the best submodular function score under a given constraint (e.g., a length limit), where the submodular function score is computed over the set of nodes in this path. This problem can be used in many applications. For example, tourists may want to search the most diversified path (e.g., a path passing by the most diverse facilities such as parks and museums) given that the traveling time is less than 6 hours. We show that the CPS-SM problem is NP-hard. We first propose a concept called “submodular$\alpha$-dominance” by utilizing the submodular function properties, and we develop an algorithm with a guaranteed error bound based on this concept. By relaxing the submodular$\alpha$-dominance conditions, we design another more efficient algorithm that has the same error bound. We also utilize the way of bi-directional path search to further improve the efficiency of the algorithms. We finally propose a heuristic algorithm that is efficient yet effective in practice. The experiments conducted on several real datasets show that our proposed algorithms can achieve high accuracy and are faster than one state-of-the-art method by orders of magnitude.

The problem of selecting a sequence of items that maximizes a given submodular function appears in many real-world applications. Existing study on the problem only considers uniform costs over items, but non-uniform costs on items are more general. Taking this cue, we study the problem of budgeted sequence submodular maximization (BSSM), which introduces non-uniform costs of items into the sequence selection. This problem can be found in a number of applications such as movie recommendation, course sequence design and so on. Non-uniform costs on items significantly increase the solution complexity and we prove that BSSM is NP-hard. To solve the problem, we first propose a greedy algorithm GBM with an error bound. We also design an anytime algorithm POBM based on Pareto optimization to improve the quality of solutions. Moreover, we prove that POBM can obtain approximate solutions in expected polynomial running time, and converges faster than a state-of-the-art algorithm POSEQSEL for sequence submodular maximization with cardinality constraints. We further introduce optimizations to speed up POBM. Experimental results on both synthetic and real-world datasets demonstrate the performance of our new algorithms.

Dynamic multiobjective optimization problem (DMOP) denotes the multiobjective optimization problem, which contains objectives that may vary over time. Due to the widespread applications of DMOP existed in reality, DMOP has attracted much research attention in the last decade. In this article, we propose to solve DMOPs via an autoencoding evolutionary search. In particular, for tracking the dynamic changes of a given DMOP, an autoencoder is derived to predict the moving of the Pareto-optimal solutions based on the nondominated solutions obtained before the dynamic occurs. This autoencoder can be easily integrated into the existing multiobjective evolutionary algorithms (EAs), for example, NSGA-II, MOEA/D, etc., for solving DMOP. In contrast to the existing approaches, the proposed prediction method holds a closed-form solution, which thus will not bring much computational burden in the iterative evolutionary search process. Furthermore, the proposed prediction of dynamic change is automatically learned from the nondominated solutions found along the dynamic optimization process, which could provide more accurate Pareto-optimal solution prediction. To investigate the performance of the proposed autoencoding evolutionary search for solving DMOP, comprehensive empirical studies have been conducted by comparing three state-of-the-art prediction-based dynamic multiobjective EAs. The results obtained on the commonly used DMOP benchmarks confirmed the efficacy of the proposed method.

Evolutionary multitasking (EMT) is a newly emerging research topic in the community of evolutionary computation, which aims to improve the convergence characteristic across multiple distinct optimization tasks simultaneously by triggering knowledge transfer among them. Unfortunately, most of the existing EMT algorithms are only capable of boosting the optimization performance for homogeneous problems which explicitly share the same (or similar) fitness landscapes. Seldom efforts have been devoted to generalize the EMT for solving heterogeneous problems. A few preliminary studies employ domain adaptation techniques to enhance the transferability between two distinct tasks. However, almost all of these methods encounter a severe issue which is the so-called degradation of intertask mapping. Keeping this in mind, a novel rank loss function for acquiring a superior intertask mapping is proposed in this article. In particular, with an evolutionary-path-based representation model for optimization instance, an analytical solution of affine transformation for bridging the gap between two distinct problems is mathematically derived from the proposed rank loss function. It is worth mentioning that the proposed mapping-based transferability enhancement technique can be seamlessly embedded into an EMT paradigm. Finally, the efficacy of our proposed method against several state-of-the-art EMTs is verified experimentally on a number of synthetic multitasking and many-tasking benchmark problems, as well as a practical case study.

For solving large-scale multiobjective problems (LSMOPs), the transformation-based methods have shown promising search efficiency, which varies the original problem as a new simplified problem and performs the optimization in simplified spaces instead of the original problem space. Owing to the useful information provided by the simplified searching space, the performance of LSMOPs has been improved to some extent. However, it is worth noting that the original problem has changed after the variation, and there is thus no guarantee of the preservation of the original global or near-global optimum in the newly generated space. In this article, we propose to solve LSMOPs via a multivariation multifactorial evolutionary algorithm. In contrast to existing transformation-based methods, the proposed approach intends to conduct an evolutionary search on both the original space of the LSMOP and multiple simplified spaces constructed in a multivariation manner concurrently. In this way, useful traits found along the search can be seamlessly transferred from the simplified problem spaces to the original problem space toward efficient problem solving. Besides, since the evolutionary search is also performed in the original problem space, preserving the original global optimal solution can be guaranteed. To evaluate the performance of the proposed framework, comprehensive empirical studies are carried out on a set of LSMOPs with two to three objectives and 500–5000 variables. The experimental results highlight the efficiency and effectiveness of the proposed method compared to the state-of-the-art methods for large-scale multiobjective optimization.

Evolutionary multitasking (EMT), which shares knowledge across multiple tasks while the optimization progresses online, has demonstrated superior performance in terms of both optimization quality and convergence speed over its single-task counterpart in solving complex optimization problems. However, most of the existing EMT algorithms only consider handling two tasks simultaneously. As the computational cost incurred in the evolutionary search and knowledge transfer increased rapidly with the number of optimization tasks, these EMT algorithms cannot meet today’s requirements of optimization service on the cloud for many real-world applications, where hundreds or thousands of optimization requests (labeled as large-scale EMT) are often received simultaneously and require to be optimized in a short time. Recently, graphics processing unit (GPU) computing has attracted extensive attention to accelerate the applications possessing large-scale data volume that are traditionally handled by the central processing unit (CPU). Taking this cue, toward large-scale EMT, in this article, we propose a new EMT paradigm based on the island model with the compute unified device architecture (CUDA), which is able to handle a large number of continuous optimization tasks efficiently and effectively. Moreover, under the proposed paradigm, we develop the GPU-basedimplicitandexplicitknowledge transfer mechanisms for EMT. To evaluate the performance of the proposed paradigm, comprehensive empirical studies have been conducted against its CPU-based counterpart in large-scale EMT.

Dynamic multiobjective optimization problem (DMOP) denotes the multiobjective optimization problem which varies over time. As changes in DMOP may exist some patterns that are predictable, to solve DMOP, a number of research efforts have been made to develop evolutionary search with prediction approaches to estimate the changes of the problem. A common practice of existing prediction approaches is to predict the change of Pareto-optimal solutions (POS) based on the historical solutions obtained in the decision space. However, the change of a DMOP may occur in both decision and objective spaces. Prediction only in the decision space thus may not be able to give the proper estimation of the problem change. Taking this cue, in this article, we propose an evolutionary search with multiview prediction for solving DMOP. In contrast to existing prediction methods, the proposed approach conducts prediction from the views of both decision and objective spaces. To estimate dynamic changes in DMOP, a kernelized autoencoding model is derived to perform the multiview prediction in a reproducing kernel Hilbert space (RKHS), which holds a closed-form solution. To examine the performance of the proposed method, comprehensive empirical studies on the commonly used DMOP benchmarks, as well as a real-world case study on the movie recommendation problem, are presented. The obtained experimental results verified the efficacy of the proposed method for solving both benchmark and real-world DMOPs.

The Vehicle Routing Problem (VRP) is a well-known NP-hard combinatorial optimization problem, which has wide spread applications in real world, such as logistics, bus route planning, and urban path planning. To solve VRP, traditional optimization methods usually start the search from scratch and ignore the VRPs solved in the past, which could lead to repeated explorations of the search space of related problems, and thus results in slow optimization process involving unnecessary computational cost. Keeping this in mind, to speed up the optimization for vehicle routing, this article presents a new study towards faster vehicle routing by transferring knowledge from customer representations which are learned from past solved VRPs. In particular, we propose to capture the useful traits buried in previous optimized routing solutions by learning a new customer representation, which can be transferred across VRPs, serving as the prior knowledge, to bias the optimization in the target VRP. In contrast to existing approaches, the proposed knowledge transfer is consist of a learning of new customer representation based on the optimized routing solution, which is general to VRPs possessing different structural properties, and a weighted$l_{1}$norm-regularized formulation for building sparse mapping across VRPs, that is easy to solve. Further, the proposed knowledge transfer across VRPs occurs along the whole optimization search process, and is thus able to guide the routing optimization process consistently. To verify the efficacy of the proposed method, by using population-based optimization method as the VRP solver, comprehensive empirical studies on both commonly used VRP benchmarks and real world vehicle routing application are presented.

Sharing transportation systems can significantly promote travelers convenience and efficiency. As a vital part, bike-sharing system (BSS) has effectively solved “the-last-mile” problem during transportation interchange, but bike imbalance between docks severely deteriorates operational efficiency of BSS. In this paper, we present multi-objective optimization and predictive control approaches to tackle the bike rebalancing problem, where optimal redistributing strategies can maximize the operational efficiency of BSS with respects to equilibrium state and redistribution cost. A dock-based BSS dynamic network is modeled based on a proximity graph, in which connection relation, bike usage, and redistribution flow are formulated. To measure the operational efficiency, a performance metric is presented to consider both benefits of user and operator. To satisfy bike renting, returning, and redistributing requirements, model predictive control (MPC) is then employed to compute feasible and optimal redistribution strategies based on the network model. The effectiveness of multi-objective optimization and MPC is verified on different topologies of BSS. Experimental results show that when the BSS reaches the equilibrium state, the operational efficiency will be maximized in the proposed approaches.

Mobile edge computing (MEC) can significantly improve the performance of mobile applications by leveraging nearby servers as the edge cloud to provide task offloading execution service for a smart mobile device (SMD) through wireless access points (APs). However, the edge cloud and AP will not provide free services. Their radio frequency resources and computing resource are limited but the service requests from various mobile devices could be massive. The goal of this article is to provide a pricing mechanism to efficiently allocate limited resources in the MEC system according to the budget of SMDs. To this end, we first present a market model of MEC resources that can give a real insight into the incentives for resource sharing at network edges. In the model, computation, and radio resources can be traded between resource suppliers (AP and edge cloud) and buyers (SMDs). Furthermore, we employ the microeconomic theory to get an optimal budget allocation strategy for the SMD to maximize its utility within a limited budget. Moreover, we propose an Equilibrium Price Finding (EPF) algorithm to find the equilibrium price of the MEC system, maximizing the whole system utility and leading to optimal resource allocation. Finally, simulation results show that, compared with state-of-the-art resource allocation methods, our optimal budget allocation algorithm can find budget allocation strategy more effectively and our equilibrium price finding algorithm can achieve market equilibrium to optimally allocate computation and radio resources in the MEC system.

Workflow scheduling problem (WSP) is a well-known combinatorial optimization problem, which is defined to assign a series of interconnected tasks to the available resources to meet user defined Quality of Service (QoS). The guided random search methods and heuristic based methods are two most common methods for solving WSP. However, these methods either require expensive computational cost or heavily rely on human's empirical knowledge, which makes them inconvenient for practical applications. Keeping this in mind, this paper proposes a cooperative coevolution hyper-heuristic framework to solve WSP with an objective of minimizing the completed time of workflow. In particular, in the proposed framework, two heuristic rules, namely, the task selection rule (TSR) and the resource selection rule (RSR), are learned automatically by a cooperative coevolution genetic programming (CCGP) algorithm. The TSR is used to select a ready task for scheduling, while the RSR is used to allocate resources to perform the selected task. To improve the search efficiency, a set of low-level heuristics are defined and used as building blocks to construct the TSR and RSR. Further, to validate the effectiveness of the proposed framework, randomly generated workflow instances and four real-world workflows are used as test cases in the experimental study. Compared with several state-of-the-art methods, e.g., the Heterogeneous Earliest Finish Time (HEFT) and the Predict Earliest Finish Time (PEFT), the high-level heuristics found by our proposed framework demonstrate superior performance on all the test cases in terms of several metrics including the schedule length ratio, speedup and efficiency.

Multi-task actor-critic is a learning paradigm proposed in the literature to improve the learning efficiency of multiple actor-critics by sharing the learned policies across tasks while the reinforcement learning progresses online. However, existing multi-task actor-critic algorithms can only handle reinforcement learning tasks within the same problem domain, they may fail in cases where tasks possessing diverse state-action spaces. Taking this cue, in this paper, we embark a study on multi-task actor-critic with knowledge transfer via a share critic to enable the multi-task learning of actor-critic in heterogeneous state-action environments. Further, for efficient learning of the proposed multi-task actor-critic, a new formula for calculating the gradient of the actor network is also presented. To evaluate the performance of our approach, comprehensive empirical studies on continuous robotic tasks with different numbers of links. The experimental results confirmed the effectiveness of the proposed multi-task actor-critic algorithm.

Since multi-objective optimization (MOO) involves multiple conflicting objectives, the high dimensionality of the solution space has a much more severe impact on multi-objective problems than single-objective optimization. Taking the advantage of random embedding, some related works have been proposed to scale derivative-free MOO methods to high-dimensional functions. However, with the premise of "low effective dimensionality", a single randomly embedded subspace cannot guarantee the effectiveness of obtained solutions. Taking this cue, we propose an evolutionary multitasking paradigm for multi-objective optimization via random embedding (EMT-ReMO) to enhance the efficiency and effectiveness of current embedding-based methods in solving high-dimensional optimization problems with low effective dimensions. In EMT-ReMO, the target problem is firstly embedded into multiple low-dimensional subspaces by using different random embeddings, aiming to build up a multi-task environment for identifying the underlying effective subspace. Then the implicit multi-objective evolutionary multitasking is performed with seamless knowledge transfer to enhance the optimization process. Experimental results obtained on six high-dimensional MOO functions with or without low effective dimensions have confirmed the effectiveness as well as the efficiency of the proposed EMT-ReMO.

Efficient optimization is a key enabler for emerging intelligent applications in Internet of Vehicles (IoV). However, existing studies in IoV only focus on solving a single domain-specific optimization problem at a time, which undermines their efficiency on tackling various cross-domain optimization tasks in IoV. In this paper, we make the first effort on investigating a novel optimization framework in IoV for cross-domain tasks via vehicular edge computing. Specifically, two typical cross-domain tasks in IoV are presented, namely, the data dissemination (DD) task and the computing offloading (CO) task. Then, a cross-domain problem called DD-CO is formulated to facilitate the sharing of task features and knowledge during the solution searching. On this basis, we propose an evolutionary multitasking approach named EMA, which consists of an integer based unified representation scheme for encoding both the DD and CO tasks in a single solution, a corresponding decoding operator for task-specific solution evaluation, and a new population evolution mechanism for better adaptation to the cross-domain problem optimization. Finally, we build the simulation model and give a comprehensive performance evaluation, which demonstrate the advancement of the new optimization framework via vehicular edge computing and the effectiveness of the proposed EMA method.

Evolutionary Multi-Tasking (EMT), which solves multiple optimization tasks simultaneously, is a burgeoning topic in the area of evolutionary computation. As the EMT transfers useful knowledge across tasks to guide the search while the optimization process progresses online, superior search performance has been obtained in many recent attempts. Autoencoding evolutionary multitasking is a recently proposed EMT algorithm, which employs a single-layer denoising auto-encoder for knowledge transfer. However, since the autoencoding evolutionary multitasking (AEEMT) algorithm learns the relationships between tasks through a linear auto-encoder, it may lead to negative transfer in cases that the linear variable relationship does not hold across tasks. Taking this cue, we propose an evolutionary multitasking algorithm with artificial neural networks for transferring knowledge across tasks in this paper, which possesses higher modeling capability of variable relationships. To test and verify the efficiency and effectiveness of the proposed model, we conducted comprehensive empirical studies on the well-known single-objectives multi-task optimization benchmarks. The experimental results have shown that the proposed method has a remarkable effect on the evolutionary search in terms of both search speed and solution quality and comparing to the autoencoding evolutionary multitasking algorithms and the single-task evolutionary counterpart.

Recently, evolutionary multitasking (EMT) has been proposed in the field of evolutionary computation as a new search paradigm, for solving multiple optimization tasks simultaneously. By sharing useful traits found along the evolutionary search process across different optimization tasks, the optimization performance on each task could be enhanced. The autoencoding-based EMT is a recently proposed EMT algorithm. In contrast to most existing EMT algorithms, which conduct knowledge transfer across tasks implicitly via crossover, it intends to perform knowledge transfer explicitly among tasks in the form of task solutions, which enables the employment of task-specific search mechanisms for different optimization tasks in EMT. However, the autoencoding-based explicit EMT can only work on continuous optimization problems. It will fail on combinatorial optimization problems, which widely exist in real-world applications, such as scheduling problem, routing problem, and assignment problem. To the best of our knowledge, there is no existing effort working on explicit EMT for combinatorial optimization problems. Taking this cue, in this article, we thus embark on a study toward explicit EMT for combinatorial optimization. In particular, by using vehicle routing as an illustrative combinatorial optimization problem, the proposed explicit EMT algorithm (EEMTA) mainly contains a weighted l1-norm-regularized learning process for capturing the transfer mapping, and a solution-based knowledge transfer process across vehicle routing problems (VRPs). To evaluate the efficacy of the proposed EEMTA, comprehensive empirical studies have been conducted with the commonly used vehicle routing benchmarks in multitasking environment, against both the state-of-the-art EMT algorithm and the traditional single-task evolutionary solvers. Finally, a real-world combinatorial optimization application, that is, the package delivery problem (PDP), is also presented to further confirm the efficacy of the proposed algorithm.

With the emergence of crowdshipping and sharing economy, vehicle routing problem with occasional drivers (VRPOD) has been recently proposed to involve occasional drivers with private vehicles for the delivery of goods. In this article, we present a generalized variant of VRPOD, namely, the vehicle routing problem with heterogeneous capacity, time window, and occasional driver (VRPHTO), by taking the capacity heterogeneity and time window of vehicles into consideration. Furthermore, to meet the requirement in today's cloud computing service, wherein multiple optimization tasks may need to be solved at the same time, we propose a novel evolutionary multitasking algorithm (EMA) to optimize multiple VRPHTOs simultaneously with a single population. Finally, 56 new VRPHTO instances are generated based on the existing common vehicle routing benchmarks. Comprehensive empirical studies are conducted to illustrate the benefits of the new VRPHTOs and to verify the efficacy of the proposed EMA for multitasking against a state-of-art single task evolutionary solver. The obtained results showed that the employment of occasional drivers could significantly reduce the routing cost, and the proposed EMA is not only able to solve multiple VRPHTOs simultaneously but also can achieve enhanced optimization performance via the knowledge transfer between tasks along the evolutionary search process.

A multifactorial evolutionary algorithm (MFEA) is a recently proposed algorithm for evolutionary multitasking, which optimizes multiple optimization tasks simultaneously. With the design of knowledge transfer among different tasks, MFEA has demonstrated the capability to outperform its single-task counterpart in terms of both convergence speed and solution quality. In MFEA, the knowledge transfer across tasks is realized via the crossover between solutions that possess different skill factors. This crossover is thus essential to the performance of MFEA. However, we note that the present MFEA and most of its existing variants only employ a single crossover for knowledge transfer, and fix it throughout the evolutionary search process. As different crossover operators have a unique bias in generating offspring, the appropriate configuration of crossover for knowledge transfer in MFEA is necessary toward robust search performance, for solving different problems. Nevertheless, to the best of our knowledge, there is no effort being conducted on the adaptive configuration of crossovers in MFEA for knowledge transfer, and this article thus presents an attempt to fill this gap. In particular, here, we first investigate how different types of crossover affect the knowledge transfer in MFEA on both single-objective (SO) and multiobjective (MO) continuous optimization problems. Furthermore, toward robust and efficient multitask optimization performance, we propose a new MFEA with adaptive knowledge transfer (MFEA-AKT), in which the crossover operator employed for knowledge transfer is self-adapted based on the information collected along the evolutionary search process. To verify the effectiveness of the proposed method, comprehensive empirical studies on both SO and MO multitask benchmarks have been conducted. The experimental results show that the proposed MFEA-AKT is able to identify the appropriate knowledge transfer crossover for different optimization problems and even at different optimization stages along the search, which thus leads to superior or competitive performances when compared to the MFEAs with fixed knowledge transfer crossover operators.

The design of the evolutionary algorithm with learning capability from past search experiences has attracted growing research interests in recent years. It has been demonstrated that the knowledge embedded in the past search experience can greatly speed up the evolutionary process if properly harnessed. Autoencoding evolutionary search (AEES) is a recently proposed search paradigm, which employs a single-layer denoising autoencoder to build the mapping between two problems by configuring the solutions of each problem as the input and output for the autoencoder, respectively. The learned mapping makes it possible to perform knowledge transfer across heterogeneous problem domains with diverse properties. It has shown a promising performance of learning and transferring the knowledge from past search experiences to facilitate the evolutionary search on a variety of optimization problems. However, despite the success enjoyed by AEES, the linear autoencoding model cannot capture the nonlinear relationship between the solution sets used in the mapping construction. Taking this cue, in this article, we devise a kernelized autoencoder to construct the mapping in a reproducing kernel Hilbert space (RKHS), where the nonlinearity among problem solutions can be captured easily. Importantly, the proposed kernelized autoencoding method also holds a closed-form solution which will not bring much computational burden in the evolutionary search. Furthermore, a kernelized autoencoding evolutionary-search (KAES) paradigm is proposed that adaptively selects the linear and kernelized autoencoding along the search process in pursuit of effective knowledge transfer across problem domains. To validate the efficacy of the proposed KAES, comprehensive empirical studies on both benchmark multiobjective optimization problems as well as real-world vehicle crashworthiness design problem are presented.

Evolutionary algorithms (EAs) often lose their superiority and effectiveness when applied to large-scale optimization problems. In the literature, many research studies have been proposed to improve the search performance of EAs, such as cooperative co-evolution, embedding, and new search operator design. Among those, memetic multi-agent optimization (MeMAO) is a recently proposed paradigm for high-dimensional problems by using random embeddings. It demonstrated high efficacy with the assumption of “effective dimension However, as prior knowledge is always unknown for a given problem, this method may fail on the large-scale problems that do not have low effective dimensions. Taking this cue, we propose an evolutionary multitasking (EMT) assisted random embedding method (EMT-RE) for solving large-scale optimization problems. Instead of conducting a search on the randomly embedded space directly, we treat the embedded task as the auxiliary task for the given problem. By performing EMT with both the given problem and the randomly embedded task, not only the useful solutions found along the search can be transferred across tasks toward efficient problem solving, but the effectiveness of the search on problems Without a low effective dimensionality is also guaranteed. To evaluate the performance of newly proposed EMT-RE, comprehensive empirical studies are carried out on 8 synthetic continuous optimization functions with up to 2,000 dimensions.

Genetic programming (GP) is a powerful tool for knowledge discovery and data mining. Over the past decades, GP has been implemented in various parallel computing platforms to reduce its search time. However, these parallel GPs have different design principles and performance characteristics, which makes it difficult for users to choose the proper parallel GP in practice. To address this issue, this paper focuses on comparing and analyzing the characteristics of parallel GPs implemented in different computing platforms, in terms of running time, the speedup ratio, and the scalability. Based on the empirical results, the guidance of selecting different parallel GPs is concluded.

Solving dynamic multi-objective optimization problem (DMOP) requires optimizing multiple conflicting objectives simultaneously. When a dynamic is detected in the changing environment, most of existing prediction-based strategies predict the trajectory of changing Pareto-optimal solutions (POS), based on the historical solutions obtained in the solution space. In this paper, we present a new prediction method to track the moving optima for solving DMOP. In contrast to existing approaches, we propose to build the prediction model in the objective space. As the evaluation for solving a DMOP is based on the Pareto-optimal front (POF), to predict directly in the objective space could provide more useful information than the prediction in the solution space. In particular, to efficiently capture the complex relationships among POFs found along the evolutionary search, here we build a prediction model in Reproducing Kernel Hilbert Space, which holds a closed-form solution. To evaluate the performance of the proposed method, empirical studies have been conducted by comparing against three state-of-the-art prediction-based strategies on fourteen commonly used DMOP benchmarks. The results obtained by using different optimization solvers confirmed the superiority of the proposed method for solving DMOP in terms of both solution quality and time efficiency.

Fast and accurate depth estimation, or stereo matching, is essential in embedded stereo vision systems, requiring substantial design effort to achieve an appropriate balance among accuracy, speed and hardware cost. To reduce the design effort and achieve the right balance, we propose FP-Stereo for building high-performance stereo matching pipelines on FPGAs automatically. FP-Stereo consists of an open-source hardware-efficient library, allowing designers to obtain the desired implementation instantly. Diverse methods are supported in our library for each stage of the stereo matching pipeline and a series of techniques are developed to exploit the parallelism and reduce the resource overhead. To improve the usability, FP-Stereo can generate synthesizable C code of the FPGA accelerator with our optimized HLS templates automatically. To guide users for the right design choice meeting specific application requirements, detailed comparisons are performed on various configurations of our library to investigate the accuracy/speed/cost trade-off. Experimental results also show that FP-Stereo outperforms the state-of-the-art FPGA design from all aspects, including 6.08% lower error, 2x faster speed, 30% less resource usage and 40% less energy consumption. Compared to GPU designs, FP-Stereo achieves the same accuracy at a competitive speed while consuming much less energy.

Fusion ART is an enhanced version of Adaptive Resonance Theory (ART) which is derived from a biologically-plausible theory of human cognitive information processing. Due to its well-established ability of learning associative mappings across multimodal pattern channels in an online and incremental manner, fusion ART has been widely applied in many real world learning problems. In this paper, we take a Fusion Architecture for Learning, Cognition, and Navigation (FALCON) as the specification and essential backbone of fusion ART and introduce an intensity attenuation controller δ for adaptively adjusting the intensity of information captured from the environment, by taking inspiration from Broadbent-Treisman Filter-Attenuation's perceptual model of environmental attention. Particularly, we propose both an adaptive δ detection algorithm as well as a δ-based pruning algorithm to enhance the learning performance of FALCON while reduce the redundant memory storage incurred by the "detrimental δ". To verify the effectiveness and efficiency of our proposed method, comprehensive experimental studies are carried out on a classical minefield navigation task.

Recent studies on three-dimensional(3D) point cloud data (PCD) simplification have played significant roles in computer-aided models for alleviating computational and storage burden. However, existing simplification methods are not suitable for the large-scale PCD with even billions of points. In this paper, a decomposed simplification method based on graph filter is developed to extract Haar-like feature of PCD. The new method is based on divide-and-conquer philosophy and thus effectively reduce the memory usage. In the proposed approach, point cloud is divided into several subsets according to the relationship of natural neighbor, and decomposed graph filtering with adaptive resampling rate is designed. Validation experiment is conducted on large scale PCD, which could indicate the effectiveness and feasibility of the proposed approach.

In the last decades, evolutionary algorithms (EAs) have demonstrated strong search capabilities in solving multi-objective optimization problems (MOPs). To improve the search performance of EAs, as problems seldom exist in isolation, transferring knowledge from related problems have attracted considerable attentions in recent years. In this paper, we present a preliminary study to enhance existing evolutionary algorithms (MOEAs) by transferring knowledge from the process of solving the single objectives involved in a given MOP of interest. As the single objectives are the objectives of the MOP, they naturally share great similarity with the given MOP, which thus could yield useful traits for enhancing the problem-solving of the MOP. To the best of our knowledge, this work severs as the first attempt to improve evolutionary multi-objective optimization via transferring knowledge from single objective problems. To evaluate the performance of the proposed method, empirical studies using a popular MOEA, i.e., NSGAII, on commonly used multi-objective benchmarks are conducted. The obtained results confirmed the efficacy of the proposed method in terms of both convergence speed and solution quality.

Edge cache service (ECS), as a prospective edge network service paradigm, can significantly reduce the data transmission latency and improve the Quality of Service (QoS) of digital content providers by offloading content data to edge devices in the network. Compared to centralized content service, ECS can provide digital content from nearby edge devices via a high-speed wireless network with fewer hops. However, how to motivate edge devices to share their cache resource and ensure the reliability of content data under the diversity of device behavior remains a challenging issue. In this article, we aim to design an ECS framework for cache resource trading and digital content sharing in the edge network. By using blockchain-based credentials, we first provide the cache resource trading mechanism for the trading between the content provider and edge devices. Then, we give a double auction mechanism for digital content trading between edge devices. The experimental results show the proposed framework can greatly improve the matching efficiency of cache resources and reduce the data transmission overhead in edge networks.

High-level synthesis (HLS) relies on the use of synthesis directives to generate digital designs meeting a set of specifications. However, the selection of directives depends largely on designer experience and knowledge of the target architecture and digital design. Existing automated methods of directive selection are very limited in scope and capability to analyze complex design descriptions in high-level languages to be synthesized using HLS. This paper proposes a comprehensive model-based analysis (COMBA) framework which is capable of analyzing the effects of a multitude of directives related to functions, loops and arrays in the design description using pluggable analytical models, a recursive data collector and a metric-guided design space exploration (DSE) algorithm. COMBA reports a small average error in estimating performance when compared with HLS tools like Vivado HLS, and finds a high-performance configuration of synthesis directives within minutes. Given different resource constraints, COMBA finds configurations with higher speed-ups, compared with the state-of-the-art. Moreover, COMBA can guide the performance and area trade-off analysis. Experiments show that our DSE algorithm outperforms the conventional genetic algorithm, and COMBA efficiently finds a near-optimal configuration, which proves the efficiency of our tool for optimizing the practical HLS based designs.

Massive and redundant vehicle trajectory data are continuously sent to the data center via vehicle-mounted GPS devices, causing a number of sustainable issues, such as storage, communication, and computation. Online trajectory compression becomes a promising way to alleviate these issues. In this paper, we present an online trajectory compression framework running under the mobile environment. The framework consists of two phases, i.e., online trajectory mapping and trajectory compression. In the phase of online trajectory mapping, we develop a light-weighted yet efficient map matcher, namely, Spatial-Directional Matching (SD-Matching), to align the noisy and sparse GPS points upon the underlying road network, which fully explores the usage of vehicle heading direction collected from the GPS trajectory data. In the phase of online trajectory compression, we propose a novel compressor based on the heading change at intersections, namely, Heading Change Compression (HCC), aiming at finding a concise and compact trajectory representation. Finally, we conduct experiments to evaluate the effectiveness and efficiency of the proposed framework using real-world datasets in the city of Beijing, China. We further deploy the system in the real world in the city of Chongqing, China. The experimental results demonstrate that: 1) the SD-Matching algorithm achieves a higher mean accuracy but consumes less time than the state-of-the-art algorithm, namely, Spatial-Temporal Matching (ST-Matching) and 2) the HCC algorithm also outperforms baselines in trading-off compression ratio and computation time.

Test case prioritization (TCP) is a popular regression testing technique in software engineering field. The task of TCP is to schedule the execution order of test cases so that certain objective (e.g., code coverage) can be achieved quickly. In this article, we propose an efficient ant colony system framework for the TCP problem, with the aim of maximizing the code coverage as soon as possible. In the proposed framework, an effective heuristic function is proposed to guide the ants to construct solutions based on additional statement coverage among remaining test cases. Besides, a sorting-based local search mechanism is proposed to further accelerate the convergence speed of the algorithm. Experimental results on different benchmark problems, and a real-world application, have shown that the proposed framework can outperform several state-of-the-art methods, in terms of solution quality and search efficiency.

Genetic programming (GP) is a powerful evolutionary algorithm that has been widely used for solving many real-world optimization problems. However, traditional GP can only solve a single task in one independent run, which is inefficient in cases where multiple tasks need to be solved at the same time. Recently, multifactorial optimization (MFO) has been proposed as a new evolutionary paradigm toward evolutionary multitasking. It intends to conduct evolutionary search on multiple tasks in one independent run. To enable multitasking GP, in this paper, we propose a novel multifactorial GP (MFGP) algorithm. To the best of our knowledge, this is the first attempt in the literature to conduct multitasking GP using a single population. The proposed MFGP consists of a novel scalable chromosome encoding scheme which is capable of representing multiple solutions simultaneously, and new evolutionary mechanisms for MFO based on self-learning gene expression programming. Further, comprehensive experimental studies are conducted on multitask scenarios consisting of commonly used GP benchmark problems and real world applications. The obtained empirical results confirmed the efficacy of the proposed MFGP.

Recently, evolutionary multi-tasking (EMT) has been proposed as a new evolutionary search paradigm that op-timizes multiple problems simultaneously. Due to the knowledge transfer across optimization tasks occurs along the evolutionary search process, EMT has been demonstrated to outperform the traditional single-task evolutionary search algorithms on many complex optimization problems, such as multimodal continuous optimization problems, NP-hard combinatorial optimization problems, and constrained optimization problems. Today, EMT has attracted lots of attentions, and many EMT algorithms have been proposed in the literature. The explicit EMT algorithm (EEMTA) is a recent proposed new EMT algorithm. In contrast to most of existing EMT algorithms, which employ a single population using unified space and common search operators for solving multiple problems, the EEMTA uses multiple populations which possess problem-specific solution representations and search mechanisms for different problems in evolutionary multi-tasking, which thus could lead to enhanced optimization performance. However, the original EEMTA was proposed for solving only two tasks. As knowledge transfer from inappropriate tasks may lead to negative effect on the evolutionary optimization process, additional designs of identifying task pairs for knowledge transfer is necessary in EEMTA for evolutionary multi-tasking with tasks more than two. To the best of our knowledge, there is no research effort has been conducted on this issue. Keeping this in mind, in this paper, we present a preliminary study on the task selection in EEMTA for many-task optimization. As task similarity may lose to capture the usefulness between tasks in evolutionary search, instead of using similarity measures for task selection, here we propose a credit assignment approach for selecting proper task to conduct knowledge transfer in explicit evolutionary many-tasking. The proposed approach is based on the feedbacks from the transferred solutions across tasks, which is adaptively updated along the evolutionary search. To confirm the efficacy of the proposed method, empirical studies on the many-task optimization problem, which consists of 7 commonly used optimization benchmarks, have been presented and discussed.

The Fusion Architecture for Learning, COgnition, and Navigation (FALCON) is an extension of the self-organizing neural network i.e., Adaptive Resonance Theory (ART), which has been successfully applied in many reinforcement learning tasks, and demonstrated fast and stable real-time learning capabilities. However, the learning of reinforcement FALCON relies on the positive feedbacks obtained from the environment, which may not be always available in many real-world applications. Although TD-FALCON has been proposed in the literature, to integrate the temporal difference method to estimate the payoff value when immediate reward is not available, the accuracy of the estimation also relies on the received feedback from environment. In complex environments with much delayed evaluation, the reinforcement FALCON may be hard to learn the proper knowledge to adapt in the given task quickly. To the best of our knowledge, there is no existing work has been conducted to improve the reinforcement FALCON learning in such environment. Taking this cue, inspired from the science of memetics, in this paper, we propose to improve the reinforcement FALCON learning in complex environment where positive reward is hard to achieve, via memetic automaton. In particular, by defining the particular representation of memes in the context of FALCON, the corresponding designs of meme selection and meme transmission for meme evolution are presented, to transfer the knowledge meme from well-learned agents in simple environment to improve the learning performance of FALCON agents in complex environment. Lastly, simulations of FALCON based multi-agent system using the mine navigation task platform, confirmed the efficacy of the proposed memetic model.

Cartesian Genetic Programming (CGP) is a powerful and popular tool for automatic generation of computer programs to solve user defined tasks. This paper proposes a Co-evolutionary CGP (named Co-CGP) which can automatically gain high-order knowledge to accelerate the search. In the Co-CGP, two modules are working in cooperation to solve a given problem. One module focuses on solving a series of small scale problems of the same type to generate the building blocks. Simultaneously, the second module focuses on combing the available building blocks to construct the final solution. Besides, an adaptive control strategy is introduced to automatically evaluate the effectiveness of the building blocks and adjust the search behaviour adaptively so as to improve search efficiency. The proposed Co-CGP is tested on eight problems with different complexities. Experimental results show that the Co-CGP can significantly improve the performance of CGP, in terms of both search efficiency and accuracy.

Differential evolution (DE) is a simple yet powerful evolutionary algorithm for the solving of continuous optimization problems. In the last decades, a plethora of DE variants have been proposed in the literature for enhanced optimization performance. However, most of these DE variants are designed to solve a single problem in a single run. Recently, a multifactorial DE (MFDE) has been proposed to conduct evolutionary search on multiple tasks simultaneously. Benefitting from the implicit knowledge transfer among different tasks, MFDE has demonstrated a superior performance against the single-task DE in terms of convergence speed and solution quality. In MFDE, the knowledge transfer is realized via the mutation operation conducted on solutions with different skill factors. However, despite a lot of mutation strategies suggested in the literature, the current MFDE takes DE/rand/1 as the only strategy for knowledge transfer. The impacts of different mutation strategies on the performance of MFDE is still unexplored. Taking this cue, in this paper, we embark a study to investigate how different mutation strategies for knowledge transfer affect the performance of MFDE. In particular, besides DE/rand/1, another four commonly-used mutation strategies are adapted for the purpose of multitask optimization. Further, towards effective mutation for knowledge transfer in MFDE, a new mutation strategy called DE/best/1+ρ, which is able to adjust its behavior along the search process is proposed. Lastly, comprehensive empirical studies are conducted to investigate the performance of existing and the new proposed mutation strategies on the 9 single-objective multitasking benchmarks.

To satisfy increasing computing demands, heterogeneous computing platforms are gaining attention, especially CPU-FPGA platforms. Recently, emerging tightly coupled CPU-FPGA platforms with shared coherent caches (such as the Intel HARP and IBM POWER with CAPI) have been proposed to facilitate data communication and simplify the programming model. In this work, we propose LAMA, a static analysis and dynamic control combined framework for memory access management in such platforms, to further enhance the memory access efficiency and maintain the data consistency. Based on implementation results on the real Intel HARP2 platform, LAMA is shown to improve the performance by 34% on average with low overhead.

To satisfy the increasing demands of modern computing tasks, heterogeneous computing is gaining attention. The CPU-FPGA platform is especially promising since the FPGA enables customization for diverse computing tasks to be offloaded from the CPU to boost the performance and energy efficiency. Nowadays, tightly coupled CPU-FPGA platforms with shared coherent caches (such as the Intel HARP and IBM POWER with CAPI) have been proposed for enhanced CPU-FPGA data communication efficiency and a simplified programming model. In Intel's recently released CPU-FPGA platform HARP2, there are three links between the XEON multi-core CPU and the Arria 10 FPGA, two PCIes and one QPI, with a coherent FPGA cache attached before the QPI for the quick memory access and data locality benefit. The link choice for the FPGA memory accesses will heavily influence the performance in such platforms and the race among links may violate the data consistency. In order to enhance the performance and maintain the data consistency, we propose COODA, a static and dynamic hybrid framework for memory access management in HARP2-like emerging CPU-FPGA platforms. COODA adaptively arranges the memory accesses to the preferred link to boost the FPGA cache benefit and enhance the utilization of all links. An automatic data consistency maintenance mechanism based on the static analysis is also applied by COODA to keep the whole data consistency. Based on implementation results on the real Intel HARP2 platform for diverse applications, COODA is shown to improve the performance a lot compared with the state-of-the-art methods.

With the ever-increasing demand of location-based services (LBS), Wi-Fi based indoor localization has attracted increasing attentions. This paper is dedicated to addressing two critical problems: a) signal fluctuation due to unforeseeable interferences during the offline training phase; b) insufficient real-time signal measurements at certain point due to the target movement during the online localization phase. Specifically, we first give an intensive analysis on the characteristics of received signal strength indicator (RSSI) in indoor environments with respect to both time-domain and frequency-domain. Then, inspired from the advantages of Stacked Denosing Autoencoder (SDA) in terms of recognizing and stabilizing the original features, we propose a dual-band SDA (DBSDA) based model to create more distinguishable fingerprints by extracting the RSSI features at each reference point (RP). In this model, both 2.4GHz and 5GHz RSSIs are exploited to train the SDA neural network and construct the offline fingerprint database. On this basis, we propose a data generation scheme, which is designed based on the observation that environmental interferences are similar in proximate spots. So, the designed scheme can generate signal values at certain point based on its nearby RSSI measurements when there are not enough inputs for the SDA neural network. Finally, we propose a locally weighted liner regression (LWLR) based method to predict the coordinate of the target. For performance evaluation, we implement the system prototype and give comprehensive experiments in real-world environments, which demonstrate the effectiveness and robustness of the proposed solutions.

Tools of high-level synthesis (HLS) are developed to improve the accessibility of FPGAs by allowing designer to describe hardware designs in high-level language, e.g. C/C++. However, the source codes of general applications are not structured as canonical dataflow. Furthermore, clock frequencies are powerful parameters to improve dataflow throughput but currently commercial HLS tools limit themselves to single clock domain. Consequently, in order to benefit from the multiple-clock dataflow design, designers still suffer from manually analyzing the applications, partitioning the source code into modules, optimizing them with appropriate parameters and resource allocation, and finally interconnecting them. We analyze the impact of multiple clock domains for HLS designs and present Hi-ClockFlow, an automatic HLS framework. Hi-ClockFlow can analyze the source code based on Light-HLS, our light weight HLS evaluation framework, explore the large design space, and optimize such parameters as clock frequencies and HLS directives in dataflow. By properly partitioning the source code of an application into parts with various clock domains, Hi-ClockFlow can optimize the dataflow with imbalanced modules and speed up the performance under the specific constraint of resource.

With recent development of wireless communication, sensing and computing technologies, Internet of Vehicles (IoV) has attracted great attention in both academia and industry. Services with low communication latency and high reliability are necessary to enable safety-critical applications in IoV. Nevertheless, it is challenging to satisfy the service requirement due to unique characteristics of IoV, including limited wireless communication bandwidth, high vehicle mobility, massive data transmission, and overwhelming computation overhead. In view of this, we propose a novel vehicular fog computing (VFC) architecture to explore the synergistic effect of the cloud, the static fog and the mobile fog by defining corresponding service modes. On this basis, we further formulate a task offloading model, which quantitatively analyzes the characteristics of the three service modes and enables task offloading based on particular service requirements. Finally, we implement a traffic abnormity detection and warning system based on the proposed architecture as a case study. The hardware-in-the-loop performance evaluation not only demonstrates the effectiveness of the proposed architecture, but also enlightens future research directions on developing adaptive task offloading for dynamic IoV applications.

With recent advances in wireless communications, vehicular networks have attracted great interests in both industry and academia. This work aims at proposing a novel vehicular fog computing paradigm including both the system architecture and the scheduling algorithm. Specifically, we present a hierarchical architecture, which integrates the paradigm of both fog computing and the software defined networking (SDN). Then, we formulate a novel problem called Cooperative Service in Vehicular Fog Computing (CS-VFC), which aims at maximizing the bandwidth efficiency by coordinating the service in both the fog layer and the cloud layer. We prove that CS-VFC is NP-hard. On this basis, we propose an on-line scheduling algorithm, which incorporates with the network coding and makes scheduling decisions at SDN controller. In particular, it will determine the coding policy for each cloud node, and then it will implement both the intra and inter cooperation strategies at the fog layer. Finally, we build the simulation model by implementing NS3 simulator and SUMO. A comprehensive simulation is carried out to demonstrate the superiority of the proposed system architecture and the solution.

In transfer learning , we aim to improve the predictive modeling of a target output by using the knowledge from some related source outputs. In real-world applications, the data from the target domain is often precious and hard to obtain, while the data from source domains is plentiful. Thus, since the complexity of Gaussian process based multi-task/transfer learning approaches grows cubically with the total number of source＋ target observations, the method becomes increasingly impractical for large ( > 1 0 4 ) source data inputs even with a small amount of target data. In order to scale known transfer Gaussian processes to large-scale source datasets , we propose an efficient aggregation model in this paper, which combines the predictions from distributed (small-scale) local experts in a principled manner. The proposed model inherits the advantages of single-task aggregation schemes, including efficient computation, analytically tractable inference, and straightforward parallelization during training and prediction. Further, a salient feature of the proposed method is the enhanced expressiveness in transfer learning — as a byproduct of flexible inter-task relationship modelings across different experts. When deploying such models in real-world applications, each local expert corresponds to a lightweight predictor that can be embedded in edge devices, thus catering to cases of online on-mote processing in fog computing settings.

Evolutionary multitasking (EMT) is an emerging research topic in the field of evolutionary computation. In contrast to the traditional single-task evolutionary search, EMT conducts evolutionary search on multiple tasks simultaneously. It aims to improve convergence characteristics across multiple optimization problems at once by seamlessly transferring knowledge among them. Due to the efficacy of EMT, it has attracted lots of research attentions and several EMT algorithms have been proposed in the literature. However, existing EMT algorithms are usually based on a common mode of knowledge transfer in the form of implicit genetic transfer through chromosomal crossover. This mode cannot make use of multiple biases embedded in different evolutionary search operators, which could give better search performance when properly harnessed. Keeping this in mind, this paper proposes an EMT algorithm with explicit genetic transfer across tasks, namely EMT via autoencoding, which allows the incorporation of multiple search mechanisms with different biases in the EMT paradigm. To confirm the efficacy of the proposed EMT algorithm with explicit autoencoding, comprehensive empirical studies have been conducted on both the single- and multi-objective multitask optimization problems.

Temporal information services are critical in implementing emerging intelligent transportation systems. Nevertheless, it is challenging to realize timely temporal data update and dissemination due to an intermittent wireless connection and a limited communication bandwidth in dynamic vehicular networks. Some previous studies have considered the temporal data dissemination in vehicular networks, but they are limited to the service region, which is inside the coverage of roadside units. To enhance system scalability, it is imperative to exploit the synergic effect of vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communications for providing efficient temporal information services in such an environment. With the above motivations, we propose a novel system architecture to enable efficient data scheduling in hybrid V2I/V2V communications by having the global knowledge of network resources of the system. On this basis, we formulate a temporal data upload and dissemination (TDUD) problem, aiming at optimizing two conflict objectives simultaneously, which are enhancing the data quality and improving the delivery ratio. Furthermore, we propose an evolutionary multi-objective algorithm calledMO-TDUD, which consists of a decomposition scheme for handling multiple objectives, a scalable chromosome representation forTDUDsolution encoding, and an evolutionary operator designed forTDUDsolution reproduction. The proposedMO-TDUDcan be adaptive to different requirements on data quality and delivery ratio by selecting the best solution from the derived Pareto solutions. Last but not least, we build the simulation model and implementMO-TDUDfor performance evaluation. The comprehensive simulation results demonstrate the superiority of the proposed solution.

Dynamic multi-objective optimization problem (D-MOP) is widely existed in many real-world applications. Over the years, DMOP has attracted many research attentions in the literature. The adaptive indicator-based evolutionary algorithm (IBEA2) is a recently proposed multi-objective evolutionary algorithm (MOEA). It has demonstrated strong search capability on commonly used multi-objective benchmarks over state-of-the-art MOEAs. However, as the adaptation of parameter$k$is based on the selected solutions with maximum hypervolume, this mechanism will be inappropriate if the problem changes over time. The reason is that the solutions with high hypervolume at one particular time instance may not be with high hypervolume at another if the problem changed. Keeping this in mind, inspired by the recent autoencoding evolutionary search, which is able to transfer the past search experiences to improve the evolutionary search on unseen problems, in this paper, we propose to extend the IBEA2 by adapting k with transferred high hypervolume solutions obtained before the dynamic change occurs, for solving DMOP. To evaluate the proposed method, empirical comparisons on the commonly used Farina-Deb-Amato (FDA) DMOP benchmarks, against both the IBEA2 and one recently proposed dynamic MOEA, are presented.

Heterogeneous computing brings the opportunity to catch up with the increasing demands of modern computing tasks. For this purpose, the CPU-FPGA platform is promising due to the high flexibility of FPGA, which enables customization for various computing tasks to boost performance and energy efficiency. Nowadays, shared coherent cache based CPU-FPGA systems (like Intel HARP and IBM POWER8 with CAPI) are proposed to enhance the communication efficiency between CPU and FPGA and simplify the programming model. In such systems, a coherent cache is attached to FPGA for the quick memory access from FPGA, and its behavior dominates the performance of the FPGA and the entire system. However, the FPGA execution tends to encounter severe cache misses on the FPGA cache, which degrades the FPGA acceleration benefits. To solve this problem, we propose CAMAS, a static and dynamic coordinated cache management approach to reduce the FPGA cache misses and enhance the AFU performance. In the static step, reuse distance analysis is applied to the memory access trace from FPGA to characterize the accessed cachelines into three types according to their locality level. Then a dynamic control with a learning mechanism performs bypassing or caching for the returned cachelines at the cache miss according to the corresponding type. Our approach combines compile-time analysis to determine the caching or bypassing preference with the run-time management equipped with a dynamic learning mechanism. Experiments on Polybench applications demonstrate an average performance improvement of 24.92% using CAMAS.

Design and construction of reinforced concrete (RC) structures are two important phases in a building construction project. Structural engineers are difficult to reject all rebar clashes in RC beam-column joints at the design phase. Construction engineers and steel fixers have to identify rebar spatial clashes and avoid rebar clashes in a manual way, which is tedious and time consuming. In this paper, an intelligent design method is urgent with the ability to avoid rebar clashes automatically. A novel artificial potential field (APF) approach is presented for the clash free layout of rebar in RC beam-column joints. Using the APF method, the layout of rebar can be regarded as the path planning of multi-agents. APF is used to generate the coordinate of the centerline of clash free rebars in a RC beam-column joint. Repulsive and attractive force can ensure a reachable and optimal solution. The simulation results showed that the proposed method is efficiency and accurate.

The raw vehicle trajectory data gathered by GPS devices is typically large and needs to be compressed online. However, GPS devices have limited resources, and cannot afford such burdensome task. To alleviate this issue, we design an online trajectory compression system consisting of Trajectory Mapping, Trajectory Compressing and Front-End Visualizer, which is implemented in the mobile phone to migrate the computation burdens. The proposed trajectory compression method does not need extra data during compressing suitable for online applications. Experiment results demonstrate our system has excellent performances regarding effectiveness, efficiency and so on.

This work aims to design and implement a scalable and easy-deployed indoor localization system based on particle filter and Wi-Fi fingerprint techniques. Specifically, our system leverages particle filter to estimate user's location and automatically scans Wi-Fi fingerprints. Then, we utilize the collected fingerprints to speed up the convergence of particles. Finally, the system iteratively refines the collected fingerprints by evaluating their performance duration the on-line localization phase, which is able to further enhance the positioning accuracy. We implement the system on Android platform and give a comprehensive performance evaluation by setting up the system in our lab area and comparing the algorithm with conventional fingerprint-based solutions. Experimental results demonstrate the scalability and effectiveness of the proposed solution.

Dark silicon is the phenomenon that a fraction of many-core chip has to be turned off or run in a low-power state in order to maintain the safe chip temperature. System-level thermal management techniques normally map application on non-adjacent cores, while communication efficiency among these cores will be oppositely affected over conventional network-on-chip (NoC). Recently, SMART NoC architecture is proposed, enabling single-cycle multi-hop bypass channels to be built between distant cores at runtime, to reduce communication latency. However, communication efficiency of SMART NoC will be diminished by communication contention, which will in turn decrease system performance. In this paper, we first propose an Integer-Linear Programming (ILP) model to properly address communication problem, which generates the optimal solutions with the consideration of inter-processor communication. We further present a novel heuristic algorithm for task mapping in dark silicon many-core systems, called TopoMap, on top of SMART architecture, which can effectively solve communication contention problem in polynomial time. With fine-grained consideration of chip thermal reliability and inter-processor communication, presented approaches are able to control the reconfigurability of NoC communication topology in task mapping and scheduling. Thermal-safe system is guaranteed by physically decentralized active cores, and communication overhead is reduced by the minimized communication contention and maximized bypass routing. Performance evaluation on PARSEC shows the applicability and effectiveness of the proposed techniques, which achieve on average 42.5 and 32.4 percent improvement in communication and application performance, and 32.3 percent reduction in system energy consumption, compared with state-of-the-art techniques. TopoMap only introduces 1.8 percent performance difference compared to ILP model and is more scalable to large-size NoCs.

High-level synthesis (HLS) of field programmable gate array (FPGA)-based accelerators has been proposed in order to simplify accelerator design process with respect to design time and complexity. However, modern HLS tools do not consider dynamic memory allocation constructs in high-level programming languages like C and limit themselves to static memory allocation. This paper proposes a dynamic memory allocation and management scheme, called Hi-DMM, for inclusion in commercial HLS design flows. Hi-DMM performs source-to-source transformation of user C code with dynamic memory constructs into C-source code with the dynamic memory allocator and management scheme developed in this paper. The transformed C-source code is amenable to synthesis by commercial tools like Vivado HLS. Relying on buddy tree-based allocation schemes and efficient hardware implementation of the allocators, Hi-DMM achieves 4x speed-up in both fine-grained and coarse-grained memory allocation compared to previous works. Experimental results obtained by including Hi-DMM with Vivado-HLS show that dynamic memory allocation of FPGA memory resources can be achieved at a much lower latency with minimal resource overhead, paving the way for synthesis of dynamic memory constructs in commercial HLS flows.

This work aims at proposing a transfer learning (TL)-based framework to enhance system scalability of fingerprint-based indoor localization by reducing offline training overhead without jeopardizing the localization accuracy. The basic principle is to reshape data distributions in the target domain based on the transferred knowledge from the source domains, so that those data belonging to the same cluster will be logically closer to each other, whereas others will be further apart from each other. Specifically, the TL-based framework consists of two parts, metric learning and metric transfer, which are used to learn the distance metrics from source domains and identify the most suitable metric for the target domain, respectively. Furthermore, this work implements a prototype of the fingerprint-based indoor localization system with the proposed TL-based framework embedded. Finally, extensive real-world experiments are conducted to demonstrate the effectiveness and the generality of the TL-based framework.

Travel behavior understanding is a long-standing and critically important topic in the area of smart cities. Big volumes of various GPS-based travel data can be easily collected, among which the taxi GPS trajectory data is a typical example. However, in GPS trajectory data, there is usually little information on travelers' activities, thereby they can only support limited applications. Quite a few studies have been focused on enriching the semantic meaning for raw data, such as travel mode/purpose inferring. Unfortunately, trip purpose imputation receives relatively less attention and requires no real-time response. To narrow the gap, we propose a probabilistic two-phase framework named TripImputor, for making the real-time taxi trip purpose imputation and recommending services to passengers at their dropoff points. Specifically, in the first phase, we propose a two-stage clustering algorithm to identify candidate activity areas (CAAs) in the urban space. Then, we extract fine-granularity spatial and temporal patterns of human behaviors inside the CAAs from foursquare check-in data to approximate the priori probability for each activity, and compute the posterior probabilities (i.e., infer the trip purposes) using Bayes' theorem. In the second phase, we take a sophisticated procedure that clusters historical dropoff points and matches the dropoff clusters and CAAs to immerse the real-time response. Finally, we evaluate the effectiveness and efficiency of the proposed two-phase framework using real-world data sets, which consist of road network, check-in data generated by over 38 000 users in one year, and the large-scale taxi trip data generated by over 19 000 taxis in a month in Manhattan, New York City, USA. Experimental results demonstrate that the system is able to infer the trip purpose accurately, and can provide recommendation results to passengers within 1.6 s in Manhattan on average, just using a single normal PC.

This paper embarks the first study on exploiting the synergy between vehicular caching and network coding for enhancing the bandwidth efficiency of data broadcasting in heterogeneous vehicular networks by presenting a service architecture that exercises the software defined network concept. In particular, we consider the scenario where vehicles request a set of information and they could be served via heterogeneous wireless interfaces, such as roadside units and base stations (BSs). We formulate a novel problem of coding-assisted broadcast scheduling (CBS), aiming at maximizing the broadcast efficiency for the limited BS bandwidth by exploring the synergistic effect between vehicular caching and network coding. We prove the NP-hardness of the CBS problem by constructing a polynomial-time reduction from the simultaneous matrix completion problem. To efficiently solve the CBS problem, we employ memetic computing, which is a nature inspired computational paradigm for tackling complex problems. Specifically, we propose a memetic algorithm, which consists of a binary vector representation for encoding solutions, a fitness function for solution evaluation, a set of operators for offspring generation, a local search method for solution enhancement, and a repair operator for fixing infeasible solutions. Finally, we build the simulation model and give a comprehensive performance evaluation to demonstrate the superiority of the proposed solution.

Recent analytical studies have revealed that in spite of promising success in problem solving, the performance of evolutionary multitasking deteriorates with decreasing similarity between constitutive tasks. The present day multifactorial evolutionary algorithm (MFEA) is susceptible to negative knowledge transfer between uncorrelated tasks. To alleviate this issue, we propose a linearized domain adaptation (LDA) strategy that transforms the search space of a simple task to the search space similar to its constitutive complex task. This high order representative space resembles high correlation with its constitutive task and provides a platform for efficient knowledge transfer via crossover. The proposed framework, LDA-MFEA is tested on several benchmark problems constituting of tasks with different degrees of similarities and intersecting global optima. Experimental results demonstrate competitive performances against MFEA and shows that our proposition dramatically improves the performance relative to optimizing each task independently.

To date, dynamic vehicle routing problem (DVRP) has attracted great research attentions due to its wide range of real world applications. In contrast to traditional static vehicle routing problem, the whole routing information in DVRP is usually unknown and obtained dynamically during the routing execution process. To solve DVRP, many heuristic and metaheuristic methods have been proposed in the literature. In this paper, we present a novel evolutionary search paradigm with learning capability for solving DVRP. In particular, we propose to capture the structured knowledge from optimized routing solution in early time slot, which can be further reused to bias the customer-vehicle assignment when dynamic occurs. By extending our previous research work, the learning of useful knowledge, and the scheduling of dynamic customer requests are detailed here. Further, to evaluate the efficacy of the proposed search paradigm, comprehensive empirical studies on 21 commonly used DVRP instances with diverse properties are also reported.

Heterogeneous computing with hardware accelerators is a promising direction to overcome the power and performance walls in traditional computing systems. CPU-accelerator integrated architectures, such as CPU with ASIC or FPGA based accelerators, are able to provide customized processing according to application requirements and are thus particularly attractive to speed up computation-intensive applications. Therefore, system level simulation showing the interaction among CPUs, hardware accelerators and memory system precisely is important for performing design space exploration leading to architecture and design optimization. In this work, we present PAAS (Processor Accelerator Architecture Simulator), a system level simulator to enable cycle-accurate full system simulation of CPU-accelerator heterogeneous systems. PAAS can easily support flexible architectural configurations, such as different on-chip interconnection topologies, memory hierarchy, etc. Using PAAS, this paper also presents the analysis of the impact of different architectural configurations on the performance of benchmark applications with different execution characteristics using FPGA based accelerators. Furthermore, as an example showing the research capability of PAAS, this paper proposes and investigates a cache-partitioning scheme for improving the performance of shared-cache based CPU-FPGA systems.

With recent advances in wireless communications, vehicular networks are envisioned as a promising paradigm on achieving breakthroughs in transportation safety, efficiency, and sustainability. This work investigates data broadcast via Infrastructure-to-Vehicle (I2V) communication by exploiting the vehicular caching and network coding for enhancing bandwidth efficiency of the road-side unit (RSU). Specifically, we present an architecture for providing real-time data services via I2V communication in the service range of a RSU. Then, we investigate the problem of cache-aided data dissemination with network coding and prove that it is NP-hard. Further, we propose a memetic algorithm, which consists of a binary vector representation for encoding solutions, a fitness function for solution evaluation, a set of operators for offspring generation, a local search method for solution enhancement and a repair operator for fixing infeasible solutions. Finally, we build the simulation model and give a comprehensive performance evaluation to demonstrate the superiority of the proposed solution.

Heterogenous computing is gaining increasing attention due to its promise of high performance with low power. Shared coherent cache based CPU-FPGA platforms, like Intel HARP, are a particularly promising example of such systems with enhanced efficiency and high flexibility. In this work, we propose a hybrid strategy that relies on both static analysis of applications and dynamic control of cache based on static analysis to minimize the contention on the FPGA cache in the emerging CPU-FPGA platforms with shared coherent caches. In the static analysis, we analyze memory access patterns of the accelerated kernels on FPGA using reuse distance theory and generate kernel characteristics called Key values. Thereafter, a dynamic scheme for cache bypassing and partitioning control based on these Key values is developed to increase the cache hit rate and improve the performance. We validate our proposed strategy using a system-level architectural simulator for CPU-FPGA heterogeneous computing systems. Experiments show that the proposed strategy can increase the cache hit rate by 22.90% on average and speed up the application by up to 12.52% with negligible area overhead.

High Level Synthesis (HLS) relies on the use of synthesis pragmas to generate digital designs meeting a set of specifications. However, the selection of a set of pragmas depends largely on designer experience and knowledge of the target architecture and digital design. Existing automated methods of pragma selection are very limited in scope and capability to analyze complex design descriptions in high-level languages to be synthesized using HLS. In this paper, we propose COMBA, a comprehensive model-based analysis framework capable of analyzing the effects of a multitude of pragmas related to functions, loops and arrays in the design description using pluggable analytical models, a recursive data collector (RDC) and a metric-guided design space exploration algorithm (MGDSE). When compared with HLS tools like Vivado HLS, COMBA reports an average error of around 1% in estimating performance, while taking only a few seconds for analysis of Polybench benchmark applications and a few minutes for real-life applications like JPEG, Seidel and Rician. The synthesis pragmas recommended by COMBA result in an average 100x speed-up in performance for the analyzed applications, which establishes COMBA as a superior alternative to current state-of-the-art approaches.

In recent decades, the field of multiobjective optimization has attracted considerable interest among evolutionary computation researchers. One of the main features that makes evolutionary methods particularly appealing for multiobjective problems is the implicit parallelism offered by a population, which enables simultaneous convergence toward the entire Pareto front. While a plethora of related algorithms have been proposed till date, a common attribute among them is that they focus on efficiently solving only a single optimization problem at a time. Despite the known power of implicit parallelism, seldom has an attempt been made to multitask, i.e., to solve multiple optimization problems simultaneously. It is contended that the notion of evolutionary multitasking leads to the possibility of automated transfer of information across different optimization exercises that may share underlying similarities, thereby facilitating improved convergence characteristics. In particular, the potential for automated transfer is deemed invaluable from the standpoint of engineering design exercises where manual knowledge adaptation and reuse are routine. Accordingly, in this paper, we present a realization of the evolutionary multitasking paradigm within the domain of multiobjective optimization. The efficacy of the associated evolutionary algorithm is demonstrated on some benchmark test functions as well as on a real-world manufacturing process design problem from the composites industry.

To enhance the search performance of evolutionary algorithms, reusing knowledge captured from past optimization experiences along the search process has been proposed in the literature, and demonstrated much promise. In the literature, there are generally three types of approaches for reusing knowledge from past search experiences, namely exact storage and reuse of past solutions, the reuse of model-based information, and the reuse of structured knowledge captured from past optimized solutions. In this paper, we focus on the third type of knowledge reuse for enhancing evolutionary search. In contrast to existing works, here we focus on knowledge transfer across heterogeneous continuous optimization problems with diverse properties, such as problem dimension, number of objectives, etc., that cannot be handled by existing approaches. In particular, we propose a novel autoencoding evolutionary search paradigm with learning capability across heterogeneous problems. The essential ingredient for learning structured knowledge from search experience in our proposed paradigm is a single layer denoising autoencoder (DA), which is able to build the connections between problem domains by treating past optimized solutions as the corrupted version of the solutions for the newly encountered problem. Further, as the derived DA holds a closed-form solution, the corresponding reusing of knowledge from past search experiences will not bring much additional computational burden on the evolutionary search. To evaluate the proposed search paradigm, comprehensive empirical studies on the complex multiobjective optimization problems are presented, along with a real-world case study from the fiber-reinforced polymer composites manufacturing industry.

In this paper, we present an evolutionary transfer reinforcement learning framework (eTL) for developing intelligent agents capable of adapting to the dynamic environment of multiagent systems (MASs). Specifically, we take inspiration from Darwin's theory of natural selection and Universal Darwinism as the principal driving forces that govern the evolutionary knowledge transfer process. The essential backbone of our proposed eTL comprises several meme-inspired evolutionary mechanisms, namely meme representation, meme expression, meme assimilation, meme internal evolution, and meme external evolution. Our proposed approach constructs social selection mechanisms that are modeled after the principles of human learning to identify appropriate interacting partners. eTL also models the intrinsic parallelism of natural evolution and errors that are introduced due to the physiological limits of the agents' ability to perceive differences, so as to generate “growth” and “variation” of knowledge that agents have of the world, thus exhibiting higher adaptivity capabilities on solving complex problems. To verify the efficacy of the proposed paradigm, comprehensive investigations of the proposed eTL against existing state-of-the-art TL methods in MAS, are conducted on the “minefield navigation tasks” platform and the “Unreal Tournament 2004” first person shooter computer game, in which homogeneous and heterogeneous learning machines are considered.

Traditionally, single-objective and multi-objective optimization have only considered a single problem in one run. However, the notion of evolutionary multitasking, which aims at solving multiple optimization problems simultaneously, has recently emerged in Evolutionary Computation (EC). It is inspired by the implicit parallelism of population-based search, which attempts to take advantage of implicit genetic transfer in a multitasking environment. According to optimization literature, transforming a single-objective optimization (SOO) problem into a multi-objective optimization (MOO) problem has often been found to remove local optima. Motivated by the aforementioned idea and the concept of multitasking, in this paper, we introduce a new strategy for tackling complex multi-modal problems. In particular, we solve the original (or target) SOO task together with an artificially formulated MOO task in a multitask setting. Therein, the MOO task is expected to provide a useful inductive bias to the search progress of the target SOO task by leveraging on the transferable knowledge shared between them, thereby helping overcome local optima and effectively guiding the population towards more promising regions of the search space.

Over the years, the algorithms of evolutionary computation have emerged as popular tools for tackling complex real-world optimization problems. A common feature among these algorithms is that they focus on efficiently solving a single problem at a time. Despite the availability of a population of individuals navigating the search space, and the implicit parallelism of their collective behavior, seldom has an effort been made to multitask. Considering the power of implicit parallelism, we are drawn to the idea that population-based search strategies provide an idyllic setting for leveraging the underlying synergies between objective function landscapes of seemingly distinct optimization tasks, particularly when they are solved together with a single population of evolving individuals. As has been recently demonstrated, allowing the principles of evolution to autonomously exploit the available synergies can often lead to accelerated convergence for otherwise complex optimization tasks. With the aim of providing deeper insight into the processes of evolutionary multitasking, we present in this paper a conceptualization of what, in our opinion, is one possible interpretation of the complementarity between optimization tasks. In particular, we propose a synergy metric that captures the correlation between objective function landscapes of distinct tasks placed in synthetic multitasking environments. In the long run, it is contended that the metric will serve as an important guide toward better understanding of evolutionary multitasking, thereby facilitating the design of improved multitasking engines.

Indicator-based evolutionary algorithm (IBEA1) is a fast and effective approach for solving multiobjective optimization problems (MOPs). In the classical IBEA1, the parameter κ is predefined to amplify or shrink the indicator differences on pairwise solutions. However, the value of κ in IBEA1 needs to be carefully calibrated based on the selected indicator (e.g., hypervolume or additive e-indicator) and the encountered MOPs. In this paper, a new version of IBEA1 (labeled as IBEA2 hereafter) is proposed to adaptively adjust parameter κ for solving various MOPs. The core idea of IBEA2 is to adapt parameter κ for the purpose of selecting the subset of offspring solutions with the maximum hypervolume into the next population. Experimental studies on 44 benchmark MOPs with 2-5 objectives in jMetal verified that IBEA2 is able to find higher hypervolumes against the four classical MOEAs, which are NSGAII, SPEA2, MOEA/D and IBEA1, in the literature.

The decomposition method in multiobjective evolutionary algorithms (MOEA/D) is an effective approach to evolve solutions along predefined weight vectors for solving multiobjective optimization problems (MOPs). However, obtaining evenly distributed weight vectors for different types of MOPs is a challenge problem especially when the true Pareto fronts (PFs) are unknown before a MOEA/D starts. In this paper, a new MOEA/D with a fast hypervolume archive (called FV-MOEA/D) is proposed to adaptively adjust the weight vectors for various shapes of PFs. The core idea of FV-MOEA/D is to periodically adjust weight vectors based on solutions in the proposed archive, in which convergence and diversity are maintained by maximizing hypervolume. Experimental studies on 58 benchmark MOPs in jMetal demonstrate that the proposed FV-MOEA/D not only reached higher hypervolumes when compare to five classical MOEAs i.e., NSGAII, SPEA2, IBEA, FV-MOEA and MOEA/D, but also obtained well distributed weight vectors on PFs with different geometrical characteristics.

Memetic algorithms (MAs), which have materialized as a fusion of population based global search and individual lifetime learning (i.e., local search) in the literature, have been widely used in real world applications to solve complex optimization problems. The balance of global and local search in MA plays a key role in defining the performance of MA in problem solving. The probabilistic memetic framework (PMF) was thus introduced to model MA as a process involving the decision of embracing the separate actions of global or local search. PMF balances these two actions by governing the local search intensity of each individual based on a theoretical upper bound derived while the search progresses. To use PMF for solving combinatorial optimization problems, according to our previous study [1], we note that the appropriate selection of a distance metric for estimating the local search intensity is a critical role. Nevertheless, to the best of our knowledge, little or no research works in the literature has studied on suitable distance metric for PMF in the context of combinatorial optimization problems. In this paper, we attempt to fill this gap by presenting a preliminary study on the selection of distance metric in PMF for capacitated arc routing problem (CARP). In particular, we first analyze the suitability of 4 existing popular distance metrics used in combinatorial optimization for solving CARP. Subsequently a score based on closeness of neighborhood and fitness landscape correlation is proposed to quantify the suitability of a distance metric in estimating the local search intensity for PMF in the context of combinatorial optimization. Experimental study on 24 egl CARP benchmark instances highlighted the significance of choice of appropriate distance metric in PMF for solving combinatorial optimization problems, with 4 new best known CARP solutions established in the present study.

Heterogeneous computing is rapidly gaining increased attention due to the promise it holds in overcoming power and performance walls in traditional computing systems. With its focus on customized processing nodes dedicated to the different tasks in an application, it is hoped that these walls will be overcome. Therefore, CPU-FPGA co-architectures are also gaining ground in application areas like recognition, mining, search, datacenter etc. However, research in CPU-FPGA co-architecture is constrained by the available synthesis and simulation tools which do not provide an integrated system level simulation and architectural exploration environment. This becomes critical when we incorporate novel memory hierarchies, multi-processor chip architectures, hardware level cache coherence etc. In this paper, we describe our open source and integrated system level simulator and architecture exploration tool called HeteroSim. It supports x86 based multi-core processor combined with a FPGA via bus-based architecture. It allows integrated system level simulation and returns performance metrics to understand application performance with respect to the simulated architectural configuration.

Memetic Multi-Agent System (MeMAS) has recently emerged as a combination of memetic automaton and multi-agent system (MAS), wherein all meme-inspired agents acquire increasing learning capacity and intelligence through meme evolution. This paper further presents a study of MeMAS in developing human-like non-player characters in complex first-person shooter (FPS) games. In particular, we consider a well-known commercial FPS game, known as Unreal Tournament 2004 (UT2004), as our game of interest and discuss the details of non-player characters based on a manifestation of “Temporal Difference - Fusion Architecture for Learning and Cognition” (TDFALCON) neural network. In addition, we present a brief cross-domain study of MeMAS wherein the useful knowledge in the form of memes learned from different yet related simple domains previously solved are used to enhance learning performance of the non-player characters in UT2004. Benchmark experiments are studied to investigate the efficacy of MeMAS in UT2004 from various aspects, including learning efficiency, generalization capability, and computational cost. The empirically results indicate that the MeMAS could clearly improve the learning effectiveness and efficiency of designed non-player characters in UT2004.

Real-time and temporal information services are intrinsic characteristics in vehicular networks, where the timeliness of data dissemination and the maintenance of data quality interplay with each other and influence overall system performance. In this work, we present the system architecture where multiple road side units (RSUs) are cooperated to provide information services, and the vehicles can upload up-to-date information to RSUs via vehicle-to-infrastructure (V2I) communication. On this basis, we formulate the distributed temporal data management (DTDM) problem as a two-objective problem, which aims to enhance overall system performance on both the service quality and the service ratio simultaneously. Further, we propose a multiobjective evolutionary algorithm called MO-DTDM to obtain a set of pareto solutions and analyze how to fulfill given requirements on system performance with obtained pareto solutions. Finally, we build the simulation model and give a comprehensive performance evaluation, which demonstrates the superiority of the proposed optimization method.

The design of evolutionary algorithms has typically been focused on efficiently solving a single optimization problem at a time. Despite the implicit parallelism of population-based search, no attempt has yet been made to multitask, i.e., to solve multiple optimization problems simultaneously using a single population of evolving individuals. Accordingly, this paper introduces evolutionary multitasking as a new paradigm in the field of optimization and evolutionary computation. We first formalize the concept of evolutionary multitasking and then propose an algorithm to handle such problems. The methodology is inspired by biocultural models of multifactorial inheritance, which explain the transmission of complex developmental traits to offspring through the interactions of genetic and cultural factors. Furthermore, we develop a cross-domain optimization platform that allows one to solve diverse problems concurrently. The numerical experiments reveal several potential advantages of implicit genetic transfer in a multitasking environment. Most notably, we discover that the creation and transfer of refined genetic material can often lead to accelerated convergence for a variety of complex optimization functions.

In recent decades, a plethora of dedicated evolutionary algorithms (EAs) have been crafted to solve domain-specific complex problems more efficiently. Many advanced EAs have relied on the incorporation of domain-specific knowledge as inductive biases that is deemed to fit the problem of interest well. As such, the embedment of domain knowledge about the underlying problem within the search algorithms is becoming an established mode of enhancing evolutionary search performance. In this paper, we present a study on evolutionary memetic computing paradigm that is capable of learning and evolving knowledge meme that traverses different but related problem domains, for greater search efficiency. Focusing on combinatorial optimization as the area of study, a realization of the proposed approach is investigated on two NP-hard problem domains (i.e., capacitated vehicle routing problem and capacitated arc routing problem). Empirical studies on well-established routing problems and their respective state-of-the-art optimization solvers are presented to study the potential benefits of leveraging knowledge memes that are learned from different but related problem domains on future evolutionary search.

An important consideration of multiobjective optimization (MOO) is the quantitative metrics used for defining the optimality of different solution sets, which is also the basic principle for the design and evaluation of MOO algorithms. Although a plethora of performance metrics have been proposed in the MOO context, there has been a lack of insights on the relationships between metrics. In this paper, we first group the major MOO metrics proposed to date according to four core performance criteria considered in the literature, namely, capacity, convergence, diversity, and convergence-diversity. Then, a comprehensive study is conducted to investigate the relationships among representative group metrics, including generational distance, ϵ-indicator (I(1)ϵ+), spread (∆), generalized spread (∆∗), inverted generational distance, and hypervolume. Experimental results indicated that these six metrics show high consistencies when Pareto fronts (PFs) are convex, whereas they show certain contradictions on concave PFs.

A major drawback of evolutionary optimization approaches in the literature is the apparent lack of automated knowledge transfers and reuse across problems. Particularly, evolutionary optimization methods generally start a search from scratch or ground zero state, independent of how similar the given new problem of interest is to those optimized previously. In this paper, we present a study on the transfer of knowledge in the form of useful structured knowledge or latent patterns that are captured from previous experiences of problem-solving to enhance future evolutionary search. The essential contributions of our present study include the meme learning and meme selection processes. In contrast to existing methods, which directly store and reuse specific problem solutions or problem sub-components, the proposed approach models the structured knowledge of the strategy behind solving problems belonging to similar domain, i.e., via learning the mapping from problem to its corresponding solution, which is encoded in the form of identified knowledge representation. In this manner, knowledge transfer can be conducted across problems, from differing problem size, structure to representation, etc. A demonstrating case study on the capacitated arc routing problem (CARP) is presented. Experiments on benchmark instances of CARP verified the effectiveness of the proposed new paradigm.

Memetics is a new science that has attracted in creasing attentions in the recent decades. Beyond the formalism of simple hybrids, adaptive hybrids and memetic algorithms, the notion of memetic automaton as an adaptive entity that is self-contained and uses memes as building blocks of information is recently conceptualized in the context of computational intelligence as potential tools for effective problem-solving. Taking this cue, this paper embarks a study on Memetic Multi agent system (MeM) towards human-like social agents with memetic automaton. Particularly, we introduce a potentially rich meme-inspired design and operational model, with Darwin's theory of natural selections and Dawkins' notion of a meme as the principal driving forces behind interactions among agents, whereby memes formed the fundamental building blocks of the agents' mind universe. Experimental studies on a Mine Navigation Task indicates the modeling of memetic agents that resemble the natural way of human interaction can lead to greater level of adaptivity and effective problem-solving.

Capacitated arc routing problem (CARP) has attracted much attention due to its generality to many real world problems. Memetic algorithm (MA), among other meta-heuristic search methods, has been shown to achieve competitive performances in solving CARP ranging from small to medium size. In this paper we propose a formal probabilistic memetic algorithm for CARP that is equipped with an adaptation mechanism to control the degree of global exploration against local exploitation while the search progresses. Experimental study on benchmark instances of CARP showed that the proposed probabilistic scheme led to improved search performances when introduced into a recently proposed state-of-the-art MA. The results obtained on 24 instances of the capacitated arc routing problems highlighted the efficacy of the probabilistic scheme with 9 new best known solutions established.