Multiple Sequence Alignment (MSA) is a fundamental challenge in bioinformatics, forming the basis for addressing numerous computational biology problems, e.g., protein structure prediction and phylogenetic modeling. Evolutionary computation (EC) has been used to deal with the MSA due to its remarkable ability to unearth optimal or near-optimal solutions and various EC-based MSA methods have been proposed. However, the accuracy of existing EC-based MSA methods can be further improved by leveraging a novel evolutionary framework and designing evolutionary operators that align with the unique characteristics of MSA. Moreover, as the length and number of sequences increase, the MSA problem scales up, making it challenging for existing EC-based methods to achieve high accuracy within a reasonable timeframe. In this article, we propose a chance and control-based multi-population genetic algorithm (CC-MPGA), which incorporates a novel multi-population framework with genetic algorithm (GA). Moreover, according to the characteristics of MSA problems, the chance-based crossover operator and control-based mutation operator are designed to boost the accuracy. The proposed CC-MPGA is tested on well-known benchmark Balibase and large-scale benchmark ExtHomfam and compared with diverse MSA approaches. Experimental results show the superiority of CC-MPGA with a good balance of accuracy and speed, indicating the effectiveness and efficiency of CC-MPGA on large-scale MSA problems.

Set meal design (SMD) for online-to-offline (O2O) restaurant services presents a complex optimization problem, requiring the simultaneous satisfaction of diverse customer preferences, operational constraints, and profit maximization objective. To address this challenge, this article proposes a comprehensive mathematical formulation for the O2O-SMD problem. This formulation integrates complex operational requirements, such as dish variety, pricing, nutritional balance, and profitability, into a unified optimization problem with well-defined objective and constraints. To efficiently solve the O2O-SMD problem, we propose a tensor-based ant colony optimization (TACO) algorithm. Distinct from traditional ant colony optimization (ACO) variants, the core of TACO lies in reformulating the fundamental ACO operations into a tensor computational structure, enabling parallel optimization over O2O-SMD tasks at the algorithmic level. Furthermore, a dedicated local search strategy is integrated to refine solutions and accelerate convergence of the algorithm. The performance of TACO is evaluated on real-world restaurant data and benchmark instances. The experimental results show that TACO significantly outperforms a wide range of comparison algorithms in terms of solution quality, scalability, and computational efficiency, confirming its effectiveness and practical value for real-world O2O-SMD problems.

Mathematics is filled with conjectures that involve infinite recursive structure, representing complex structures and underlying deep relationships. Discovering such conjectures is crucial for advancing our understanding of fundamental mathematical principles, as they reveal unexpected patterns and connections across different areas of mathematics. Due to their inherent complexity and infinite nature, these conjectures are challenging to uncover using traditional methods such as manual derivation and numerical calculations. A core task in studying such conjectures is identifying recursive relationships that describe potentially unknown patterns and structures. This task can be framed as a symbolic regression problem, as it involves searching for a suitable mathematical form to represent complex relationships. To address this symbolic regression problem, we propose a gene programming-based algorithm named infinite conjecture explorer (ICE) with a dual-chromosome encoding (DCE) and a two-sided matching operator (TMO). DCE encodes the two sides of a conjectured as separate two chromosomes, providing a clear representation of the underlying structure of an equation. Unlike other encoding methods, DCE improves the efficiency of discovering meaningful conjectures. Since the use of DCE results in two corresponding large and complex search spaces, TMO is designed to efficiently identify expressions that match on both sides of the equation across two spaces. The experimental results show that the proposed ICE is effective in generating promising conjectures with diverse forms.

Bayesian optimization (BO), a data-efficient method for expensive black-box optimization, has traditionally focused on single-task scenarios, ignoring potential correlations among related tasks and leading to resource inefficiency due to repeated explorations. While existing multi-task BO methods mainly enhance surrogate models and sampling strategies, they rely on implicit knowledge transfer mechanisms that risk performance degradation from interference tasks, leveraging existing knowledge to optimize similar tasks instead of jointly optimizing multiple tasks from scratch. To address these issues, we propose a novel algorithm with adaptive knowledge transfer via kernelized autoencoding for multi-task Bayesian optimization (AKT-MTBO), which mainly has two core innovations. One is a kernel-induced task similarity measurement, where a kernelized autoencoding mechanism is employed to capture the nonlinear relationships between datasets. The other is an adaptive explicit knowledge transfer mechanism, where a heuristic rule is introduced to dynamically adjust the priority of selection of auxiliary tasks, ensuring selective collaboration while mitigating interference. Experiments on benchmark problems demonstrate that our proposed AKT-MTBO performs reliably in terms of both optimization efficiency and optimal solution success rates.

The escalating need for live video streaming has emerged as a significant catalyst for the business expansion of today’s content delivery networks (CDN). Selecting the right CDN live streaming architecture is fundamentally important in achieving the objective of enhancing users’ quality of experience (QoE) while reducing bandwidth costs. Regrettably, a limited number of studies have been conducted to systematically measure and compare the current typical solutions at production scale. Consequently, the performance and costs of different streaming architectures remain myths. This paper aims to address the existing research gap by undertaking a large-scale measurement study of three representative CDN live streaming architectures, defined by streaming protocol and overlay topology choices, currently running on Alibaba Cloud’s production video delivery network. By analyzing the results of over 500 million video plays over two months on a large live streaming platform hosted on Alibaba Cloud’s CDN, we reveal the impact of architectural compositions and operational factors on live streaming performance and bandwidth costs. In particular, our study reveals the trade-offs between QoE metrics and bandwidth costs for operational streaming architectures. Drawing upon the insights of this study, we further develop and deploy pragmatic strategies that yield remarkable real-world impact—our design saves over 17% bandwidth costs while maintaining the QoE.

Feature selection (FS) is a significant research topic in machine learning and artificial intelligence, but it becomes complicated in the high dimensional search space due to the vast number of features. Evolutionary computation (EC) has been widely used in solving FS by modeling it as an expensive wrapper-form optimization task, where a classifier is used to obtain classification accuracy for fitness evaluation (FE). In this article, we propose that the FS problem can be also modeled as a cheap filter-form optimization task, where the FE is based on the relevance and redundancy of the selected features. The wrapper-form optimization task is beneficial for classification accuracy while the filter-form optimization task has the strength of a lighter computational cost. Therefore, different from existing multitask-based FS that uses various wrapper-form optimization tasks, this article uses a multiform optimization technique to model the FS problem as a wrapper-form optimization task and a filter-form optimization task simultaneously. An evolutionary multitask FS (EMTFS) algorithm for parallel tacking these two tasks is proposed followed by, in which a two-channel knowledge transfer strategy is proposed to transfer positive knowledge across the two tasks. Experiments on widely used public datasets show that EMTFS can select as few features as possible on the premise of superior classification accuracy than the compared state-of-the-art FS algorithms.

Symbolic regression, a multifaceted optimization challenge involving the refinement of both structural components and coefficients, has gained significant research interest in recent years. The Equation Learner (EQL), a neural network designed to optimize both equation structure and coefficients through gradient-based optimization algorithms, has emerged as an important topic of concern within this field. Thus far, several variations of EQL have been introduced. Nevertheless, these existing EQL methodologies suffer from a fundamental constraint that they necessitate a predefined network structure. This limitation imposes constraints on the complexity of equations and makes them ill-suited for high-dimensional or high-order problem domains. To tackle the aforementioned shortcomings, we present a novel approach known as the evolving Equation Learner (eEQL). eEQL introduces a unique network structure characterized by automatically defined functions (ADFs). This new architectural design allows for dynamic adaptations of the network structure. Moreover, by engaging in self-learning and self-evolution during the search process, eEQL facilitates the generation of intricate, high-order, and constructive sub-functions. This enhancement can improve the accuracy and efficiency of the algorithm. To evaluate its performance, the proposed eEQL method has been tested across various datasets, including benchmark datasets, physics datasets, and real-world datasets. The results have demonstrated that our approach outperforms several well-known methods.

When solving optimization problems with expensive or implicit objective functions, evolutionary algorithms (EAs) commonly utilize surrogate models as cost-effective substitutes for evaluation. This category of algorithms is referred to as data-driven EAs (DDEAs). However, when constructing surrogate models, existing studies rely on the hand-crafted model structure, requiring prior knowledge while leading to the suboptimal fitting ability of the model. To address the issue, this article proposes a novel symbolic regression (SR)-assisted EA, namely SR-DDEA. SR-DDEA employs SR to automatically construct the model structure without prior knowledge and obtain accurate surrogates. Specifically, we develop an efficient gene expression programming algorithm to enhance the expressive ability of surrogates, assisted by a queue-based decoding strategy to improve the efficiency of the model calculations. We also employ a clustering-based selective ensemble method to maximize data utilization and obtain diverse models. Experimental findings on commonly employed benchmarks demonstrate that our algorithm surpasses other cutting-edge offline DDEAs on test problems of different scales and a practical aerodynamic airfoil design challenge.

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.

Uncrewed aerial vehicles (UAVs) are increasingly employed for data collection in wireless sensor networks (WSNs) owing to their flexibility and real-time operational capabilities. However, effective UAV path planning remains a critical research challenge, requiring the design of optimal routes to efficiently complete data collection in WSNs. This paper introduces a novel constrained UAV data collection model tailored to address real-world challenges in this domain. Traditional mathematical optimization methods often face significant difficulties in derivation and computational complexity. Similarly, classical evolutionary computation (EC) algorithms are limited by their dependence on serial computations, resulting in substantial time costs. To address these issues, we propose a matrix-based differential evolution algorithm (MDE), leveraging matrix index operations to facilitate parallel computation and solve the problem efficiently. Given that existing matrix-based evolutionary computation (MEC) algorithms have limited applications in constrained optimization problems, we further introduce a constraint-guided optimization (CGO) method, enabling the MDE algorithm to inherently support constrained optimization. Experimental results demonstrate that the proposed MDE-CGO outperforms other representative EC methods in optimizing the model of constrained UAV data collection from WSNs. Only our proposed approach successfully optimizes the model to generate feasible UAV paths in all the experiments. Moreover, a computational speed comparison highlights that the MDE-CGO not only delivers superior optimization performance but also achieves high computational efficiency.

Vehicle trajectory data are essential in smart mobility applications, yet often incomplete, necessitating systematic reconstruction for effective use. Existing methods often overlook traffic rules and vehicle interactions in their reconstruction process, a research gap that becomes critical for fine-grained reconstruction of incomplete and irregular microscopic traffic data. To address this limitation, this paper introduces a novel fine-grained trajectory reconstruction (FTR) framework, particularly for urban signalized intersections, considering both traffic rules and vehicle interactions through a microscopic traffic simulation (MTS) model. This is motivated by challenging missing patterns in real-world data from Alibaba City Brain Lab and limitations in existing reconstruction approaches. To this end, the FTR problem is first formulated as an MTS-based optimization problem. Then, to solve this problem effectively under a limited computing budget, an advanced dynamic data-driven evolutionary optimization technique, D3GA++, is proposed. Through the validation involving two real-world datasets, D3GA++ has demonstrated superior performance under various missing data scenarios consistently surpassing baselines such as brute-force random search and standard evolutionary algorithm in terms of reconstruction accuracy. Our work can have crucial implications for traffic management, urban planning, and autonomous vehicle technology development.

Crowd management plays a crucial role in improving travel efficiency and reducing potential risks caused by overcrowding in large public places. Crowd control at entrances is a common way in our daily life to avoid overcrowding, but nowadays the control of crowd inflow at the entrances of public places mainly relies on manual operation. In this article, we intend to propose a dynamic crowd inflow control system (DRIFT) to avoid risks of overcrowding and improve the throughput of public places. First, we formulate an optimization problem that maximizes throughput by adjusting the crowd inflow rate of each entrance in the public place. Through mathematical analysis and related proofs, we introduce a baseline for the aforementioned problem that can calculate the upper bound of static inflow rate. With this baseline, we can easily measure the performance of other dynamic inflow control algorithms. Second, we treat the proposed optimization problem as a real-time decision-making problem, and further propose the DRIFT system based on deep reinforcement learning to address it. Specifically, the strategy of DRIFT is a basic actor-critic framework adapting a shared long short term memory (LSTM) layer to extract scene feature information. Third, we train it through proximal policy optimization (PPO) to improve learning performance. The environment for experiments is a crowd simulation model of OpenAI Gym structure based on real scene data from the 1F floor of the Chengdudong Railway Station and Xizhimen Railway Station. In comparison experiments and ablation experiments, the strategy of our DRIFT outperforms all other comparison strategies, including the most recent strategy using reinforcement learning, in term of system crowd throughput and robustness.

Feature selection (FS), which aims to minimize the classification error and the number of selected features, can essentially be modeled as a multiobjective optimization problem. To deal with such multiobjective FS (MOFS) problems, many multiobjective evolutionary algorithms (MOEAs) have been proposed. These MOEAs can find multiple optimal solutions, whereas substantial computational resources are required for fitness evaluations (FEs). More seriously, due to the high dimensionality and sparsity of FS problems, many FEs will be spent on unpromising solutions, resulting in a meaningless loss of computational resources. In this paper, two innovations are made so as to improve the performance of MOEAs for MOFS. First, we propose a surrogate-assisted flip (SF) strategy for MOEAs to reduce the FE waste on potentially unpromising solutions and improve search efficiency. This SF strategy is free of FE consumption and theoretically can be embedded in any MOEA to deal with MOFS problems. The experimental results show that this SF can improve the performance of different MOEAs, especially in reducing the number of selected features. Second, based on SF, we propose a more efficient SF -assisted MOEA for dealing with high-dimensional MOFS problems. The proposed algorithm divides the whole search space into different subspaces based on redundant feature subsets clustering to achieve parallel search, so as to reduce the search difficulty. The experimental results show that this algorithm is even more competitive than other SF -assisted MOEAs.

Automatic implicit equation discovery is a meaningful and challenging problem in symbolic regression. The current common methods for the automatic discovery of implicit equations include derivative calculations and comprehensive learning. However, both methods come with their own set of challenges. Derivative calculations pose difficulties in handling sparse data. The comprehensive learning method may encounter problems associated with multiple multiplications, making it difficult to find the optimal equation. Inspired by Bolzano's theorem,we propose a new evaluation mechanism known as the "Sign Change Detection (SCD) based Fitness Evaluation". The main idea of our proposed mechanism is to approximate the solution of an equation using Bolzano's theorem. This mechanism can overcome the limitations associated with derivative calculations and comprehensive learning methods. Furthermore, we integrate this mechanism with self-learning gene expression programming (SL-GEP) to develop a new SCD-GEP method. Experimental results have shown that the proposed method surpasses the compared approaches in discovering implicit equations, achieving a higher success rate in finding optimal solutions.

Guidance signage placement is a fundamental operation for crowd control in public places.The currentmethods mainly rely on manual design ormathematicalmodels, which are not flexible and effective enough for crowd control in large public places. To address this issue, this article proposes a multiobjective evolutionary framework that can search for high-quality guidance signage placement strategies automatically. In the proposed method, an agent-based crowd simulation model is proposed to simulate the wayfinding behaviors of pedestrians in public places. Furthermore, a new safety metric is proposed to quantitatively evaluate the quality of guidance signage placement strategies. On this basis, an indicator-based multiobjective evolutionary algorithm (IBEA) is utilized to search for optimal guidance signage placement strategies that have tradeoffs between crowd safety and pedestrians’ travel time. Simulation experiments on both synthetic and real-world scenes were conducted to evaluate the proposed method, and the simulation results show that the proposed framework can generate very promising guidance signage placement strategies in comparison with several existing methods.

The rapid development of online social networks (OSNs) has facilitated people to express opinions and share information. To optimize the utility of information dissemination in OSNs, problems such as influence maximization have received increasing attention in recent years. However, not only positive information but also negative information is spreading in OSNs. The dissemination of positive and negative information interacts with each other, making network dissemination analysis and utility optimization more challenging. To this end, we develop a negative–neutral–positive–susceptible (NNPS) model and propose a max–min ant system algorithm with a repetitive influence reduction strategy (MMAS-RIR). First, an NNPS model with a novel heterogenous influence indicator is constructed to simulate the interactive dissemination of positive and negative information. The influence of each user’s neighbors on each user is treated differently, producing heterogenous state transition probabilities for users. Second, we formulate the control of information dissemination as an optimization problem with a designed control scheme. The disruption strategy and counterbalance strategy are automatically implemented on the selected users according to their states in the control scheme. Third, we specially develop a MMAS-RIR algorithm for the formulated problem, where the repetitive influence reduction strategy is used to reduce the influence repeated range of the connected users. Moreover, to improve the exploitation, an adaptive local search is added in MMAS-RIR. Finally, various experiments are conducted to validate the effectiveness of our work.

Crowd navigation path planning is important in public scenes. Existing strategies are mainly based on manual design, which is not flexible or effective enough. This article proposes an evolutionary framework for automatic crowd navigation path planning in public scenes. The proposed framework contains a new fitness evaluation mechanism that can quantitatively evaluate the quality of a path planning strategy by considering both crowd safety and flow speed. Based on the fitness evaluation mechanism, a framework based on multiobjective differential evolution (DE) is developed to efficiently evolve path planning strategies. Simulation results on two synthetic scenes and a real-world metro station scene show that the proposed framework can provide good path planning strategies.

Symbolic regression (SR) is an important problem with many applications, such as automatic programming tasks and data mining. Genetic programming (GP) is a commonly used technique for SR. In the past decade, a branch of GP that utilizes the program behavior to guide the search, called semantic GP (SGP), has achieved great success in solving SR problems. However, existing SGP methods only focus on the tree-based chromosome representation and usually encounter the bloat issue and unsatisfactory generalization ability. To address these issues, we propose a new semantic linear GP (SLGP) algorithm. In SLGP, we design a new chromosome representation to encode the programs and semantic information in a linear fashion. To utilize the semantic information more effectively, we further propose a novel semantic genetic operator, namely, mutate-and-divide propagation, to recursively propagate the semantic error within the linear program. The empirical results show that the proposed method has better training and test errors than the state-of-the-art algorithms in solving SR problems and can achieve a much smaller program size.

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

Self-attention-based networks have obtained impressive performance in parallel training and global context modeling. However, it is weak in local dependency capturing, especially for data with strong local correlations such as utterances. Therefore, we will mine linguistic information of the original text based on a semantic dependency and the semantic relationship between nodes is regarded as prior knowledge to revise the distribution of self-attention. On the other hand, given the strong correlation between input characters, we introduce a one-dimensional (1-D) convolution neural network (CNN) producing query(Q) and value(V) in the self-attention mechanism for a better fusion of local contextual information. Then, we migrate this variant of the self-attention networks to speech synthesis tasks and propose a non-autoregressive (NAR) neural Text-to-Speech (TTS): SeDepTTS. Experimental results show that our model yields good performance in speech synthesis. Specifically, the proposed method yields significant improvement for the processing of pause, stress, and intonation in speech.

Evolutionary multitasking (EMT), which aims to exploit effective knowledge among similar tasks to improve search efficiency, is a hot research topic that has recently attracted a lot of attention. Ant Colony Optimization (ACO), which is inspired by the foraging behavior of ant species, is a popular and powerful search algorithm for NP-hard combinatorial optimization problems. However, EMT has seldom been integrated with the ACO. Inspired by the remarkable success of multitasking evolutionary algorithms in numerous research fields, this paper proposes a multitasking ant colony optimization framework (MTACO). The proposed framework enables ants to exploit the pheromones of ant colonies with similar tasks through certain conditions to improve the efficiency and the quality of results of ACO when processing multiple similar tasks dynamically. Furthermore, a multitasking ACS (MTACS) is implemented based on the proposed MTACO framework to solve dynamic vehicle path planning problems (DVPP). The experimental results on DVPP have verified that MTACO can improve the performance of ACO in terms of both algorithm efficiency and quality of results, when ACO is handling multiple tasks simultaneously.

With the rapid development of speech conversion and speech synthesis algorithms, automatic speaker verification (ASV) systems are vulnerable to spoofing attacks. In recent years, researchers had proposed anti-spoofing systems based on hand-crafted features. However, using hand-crafted features rather than raw waveform will lose implicit information for audio anti-spoofing. Inspired by the promising performance of ConvNeXt in classification tasks, we reference the network architecture design of ConvNeXt and propose a Lightweight and Efficient Model for Audio Anti-Spoofing (LEMAAS). With no preceding feature extraction process, we employ raw waveforms as direct inputs to our proposed model. By integrating with the channel attention module and using the focal loss function, the proposed model can focus on the most informative features representation of speech and the difficult samples that are hard to classify. Experimental results show that our proposed system could achieve an equal error rate of 0.64% and min-tDCF of 0.0187 for the ASVspoof 2019 LA evaluation dataset, which outperforms the state-of-the-art systems. Moreover, even when trained only on the ASVspoof 2019 LA dataset, the model still achieved equal error rates of 0.86% and 1.18% on the ASVspoof 2015 development dataset and evaluation dataset, respectively. This demonstrates that our model has achieved promising generalization performance during cross-dataset testing.

Deploying guardrails near elevator entrances is an effective way to alleviate congestion and improve the flow rate in subway stations. How to properly design the guardrail layout is a complex black-box optimization problem. Existing methods are mainly based on manual design, which are highly dependent on the empirical experience of the designers and may not get satisfactory results in complicated scenarios. To address the above issues, this article proposes an evolutionary framework to automatically optimize guardrail layouts in subway stations. In the proposed framework, a novel guardrail layout encoding method is proposed, which can facilitate the algorithm to generate regular guardrail layout design solutions. Furthermore, a new fitness evaluation function is proposed to effectively measure the quality of a given guardrail layout design strategy. To validate its effectiveness, the proposed framework is applied to two scenarios with different characteristics. Simulation results have demonstrated that the proposed framework can provide promising guardrail layout designs, which can alleviate the congestion of subway stations effectively.

The increasing population density in public places necessitates urgent attention to address safety concerns via effective crowd management. In many congested scenarios such as peak-hour subway stations, the utilization of fences to guide crowd movement has become a widely adopted approach to alleviate congestion. This work presents a method that combines crowd simulation and management, focusing on the optimization of the fence layout for efficient crowd guidance. First, a congestion probability social force model (CP-SFM) is introduced to simulate the irrational pedestrians and to evaluate the efficacy of different fence layouts. Second, based on CP-SFM, we are the first to formulate the fence layout problem as an optimization problem with the objective to minimize the congestion of pedestrians in public places. Third, we further propose an ant colony crowd intervention algorithm (ACCI) to optimize the layout of fences. Lastly, we illustrate the performance of proposed ACCI on 18 scenarios including two real-world subway stations. Compared with other optimization methods, ACCI demonstrates promising performance in avoiding crowd congestion.

We focus on retrieval-based dialogue systems. Such a system aims to select an appropriate response from a candidate pool for a given context. Recent methods commonly utilize powerful interaction-based pre-trained language models like BERT to achieve the goal. However, their time cost is usually not satisfying since the procedure of computing relevance scores is not efficient, especially in scenarios that require online response selection. We propose an efficient dialogue system that utilizes a representation-based BERT to address this issue, which can produce an independent representation for every response candidate and context. The relevance score can be simply calculated by the dot product. We further enhance the representation ability of this model by applying domain adaptive post-training and supervised contrastive learning fine-tuning. Experimental results on two benchmark datasets show that our method achieves competitive performance with other interaction-based models while retaining the advantage of time efficiency. We also provide an empirical and theoretical analysis of time efficiency between representation-based models and interaction-based models. The main contribution of this paper is to propose a novel methodology to build a simple but efficient dialogue system.

Crowd density forecasting in transportation building has valuable applications involving security, crowd management, and service design. The existing methods lack the prediction performance to forecast long-term (minutes-long) crowd density, which specializes in being sensitive to the external condition. Thus, we propose a method that can combine dual-modal information: the surveillance video streams and the transportation schedule information to forecast the future crowd density in the transportation building. The model utilizes the temporal convolution layers to extract the time dependence of the video streams and the transportation schedule. The pooling with an assignment matrix technique is used to learn the correlation between the video and the transportation schedule information. The predictor fuses both information and uses the Gated Recurrent Unit (GRU) layers to predict the crowd density. The experimental results show that our method could effectively benefit from the dual-modal information and give more accurate prediction results.

Pedestrian intention prediction is an important issue in crowd modeling and simulation. Existing approaches focus on short term intention prediction, which limits their applications in large public places that require long term intention prediction. To this end, this paper proposes a data-driven approach to predict long term pedestrian intention. In the proposed approach, local velocity fields are constructed based on historical trajectories of pedestrians. A similarity function is further defined based on the velocity fields to predict the intermediate destinations of pedestrians. To evaluate its effectiveness, we evaluated the proposed approach in a real world example – an airport terminal. The simulation results have demonstrated that our approach can offer effective prediction performance.

Evolutionary algorithms (EAs) possess strong problem-solving abilities and have been applied in a wide range of applications. However, they still suffer from a high computational burden and poor generalization ability. To overcome the limitations, numerous studies consider conducting knowledge extraction across distinct optimization task domains. Among these research strands, one representative tributary is evolutionary multitask optimization (EMTO) that aims to resolve multiple optimization tasks simultaneously. The underlying attribute of implicit parallelism for EAs can well incorporate with the framework of EMTO, giving rise to the ascending EMTO studies. This review is intended to present a detailed exposition on the research in the EMTO area. We reveal the core components for designing the EMTO algorithms. Subsequently, we organize the works lying in the fusions between EMTO and traditional EAs. By analyzing the associations for diverse strategies in different branches of EMTO, this review uncovers the research trends and the potentially important directions, with additional interesting real-world applications mentioned.

At present, city logistics mostly adopts a two-echelon dispatching model which combines distribution centers located in suburbs and fixed satellites located in urban areas for distribution. However, both expensive rental fees and daily changes of customer demand in metropolitan areas make dispatching route generated by fixed satellites inefficient. Moreover, the existing mobile depot model needs a large investment for facilities. In this paper, we propose a two-echelon city dispatching model with mobile satellites (2ECD-MS) which locations of mobile satellites change according to demands of customers to ensure the efficiency of delivery routes in every day. A cluster-based variable neighborhood search scheduling algorithm is proposed to determine locations of mobile satellites and dispatching routes of trucks and tricycles. Then, the 2ECD-MS is extended to 2ECD-MS-TDD to allow trucks dispatching directly (TDD) for further cost reduction. Experimental results show that the 2ECD-MS significantly reduces the total cost against the model using fixed satellites mode by 3.5% while the 2ECD-MS-TDD further reduces the total cost against the 2ECD-MS significantly by 3.25% in 54 cases with different customer scales, geographical scopes, and distribution types. These show the superiority of the proposed methods in cost reduction for city logistics in comparison to the traditional fixed model.

For safety planning in crowd evacuation, it is important to predict the evacuation decisions made by different individuals and understand the reasons behind these decisions. To this end, this paper proposes an automated approach that can learn prioritized fuzzy decision rules from crowd data to predict and understand the evacuation decisions of a real human. A coevolutionary fuzzy rule miner based on genetic fuzzy-system is designed to select necessary decision features from available ones and learn both rule structure and associated rule parameters from training data. The learned fuzzy rule contains multiple sub-rules, each of which can represent evacuation strategies of different individuals in a given scenario and the features in the fuzzy condition of the sub-rule are organized and evaluated in a sequential order to reflect the priorities of different features. Based on training and testing on four evacuations scenarios of two real-world datasets, it is shown that our proposed approach can learn decision rules that are competitive to the existing evacuation decision models in terms of prediction accuracy. More importantly, it is also demonstrated that our learned rules complying with the proposed prioritized fuzzy rule representation can facilitate the interpretation of evacuation behaviors, such as “herding under zero visibility of exit” and “diminished importance on the distance to exit”, which are aligned to the field observations from real crowd evacuation.

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.

Genetic Programming (GP) is a powerful evolutionary algorithm that has a wide range of real-world applications. High-dimensional symbolic regression (HDSR) is an important yet challenging application of GP. In this paper, a comparative study is conducted to investigate and to discuss the effectiveness of dimensionality reduction (DR) techniques in assisting GP for HDSR problems. Three popular DR techniques, which are the Pearson Correlation Coefficient (PCC), the Principal Component Analysis (PCA), and the Maximal Information Coefficient (MIC), are selected for comparison and discussion. The experimental results showed that considering only correlation during DR is not effective enough to provide a suitable reduced set of problem dimensions, and that GP with DR may perform worse than its counterpart without DR. Meanwhile, we propose a novel two-phase DR method, considering both correlation and redundancy. The proposed method can give a more reasonable set of reduced dimensions, which can effectively improve the performance of GP on HDSR problems.

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.

Economic dispatching of generating units in a power system can significantly reduce the energy cost of the system. However, the economic dispatch (ED) problem is highly constrained, and often has disconnected feasible regions because of various physical features. Enhancing population diversity is critical for the evolutionary approach to fully explore and exploit the feasible regions. In this article, we propose a density-enhanced multiobjective evolutionary approach to solve ED problem. An ED problem is first transformed into a tri-objective optimization problem, and then multiobjective optimization techniques are employed to fully optimize the constraints and cost function simultaneously. The first two objectives are derived from the original ED problem, while the third one is a novel density objective constructed by niching methods to enhance population diversity. These three objectives are optimized simultaneously by a dynamic dominance relation, which can make a good balance among feasibility, diversity, and convergence. To evaluate the performance of this proposed approach, 22 benchmark problems and seven real-world ED problems with different features are tested in this article. The experimental results show that our approach performs better than or at least competitive to the state-of-the-art algorithms, especially on large-scale ED problems.

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.

In subway stations, elevators are one of the most narrowed areas that slow down the moving of crowds. A large number of passengers gather around the elevator entrances and may cause unexpected accidents such as stampede. An effective way to guide the flow of passengers is to use guardrails. So far, the arrangement of guardrails in most subway stations is still designed manually, which requires rich experience and expert knowledge. In this paper, we propose to use the multi-objective evolutionary algorithm to design the guardrails of the elevator entrance automatically. The transfer time of passengers and the flow rate are optimized concurrently. The proposed algorithm is tested in two scenarios with different complexities. Experimental results show that the proposed algorithm can provide promising guardrail arrangements, and reveal some instructive conclusions for guardrail design in subway stations.

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.

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.

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.

Multi-tasking optimization has now become a promising research topic that has attracted increasing attention from researchers. In this paper, an efficient memetic evolutionary multi-tasking optimization framework is proposed. The key idea is to use multiple subpopulations to solve multiple tasks, with each subpopulation focusing on solving a single task. A knowledge transferring crossover is proposed to transfer knowledge between subpopulations during the evolution. The proposed framework is further integrated with a multi-objective differential evolution and an adaptive local search strategy, forming a memetic multiobjective DE named MM-DE for multi-tasking optimization. The proposed MM-DE is compared with the state-of-the-art multi-tasking multi-objective evolutionary algorithm (named MO-MFEA) on nine benchmark problems in the CEC 2017 multi-tasking optimization competition. The experimental results have demonstrated that the proposed MM-DE can offer very promising performance.

This paper proposes a surrogate-assisted multitasking memetic algorithm (SaM-MA) for multi-tasking optimization. In the proposed SaM-MA, the population is divided into multiple sub-populations, with each sub-population focusing on solving one task. Each sub-population is evolved by three components. The first is the global search component which used differential evolutionary algorithm to search for the global optimal solution for the corresponding task. The second component is a surrogate model with Gaussian process, which is used to predict the best solution, so as to reduce the number of fitness evaluations and to improve the search efficiency. The third component is the local search component which utilizes the CMA-ES to locally exploiting the neighboring regions of promising solutions. In addition, the crossover operators in the global search component are extended so as to facilitate knowledge transfer between sub-populations, The proposed SaM-MA is tested on nine benchmark multi-tasking optimization problems in the CEC2017 competition. The experiment results have demonstrated the efficacy of the proposed SaM-MA in terms of solution accuracy and search efficiency.

Generating suitable game scenarios that can cater for individual players has become an emerging challenge in procedural content generation. In this paper, we propose a data-driven scenario generation framework for game-based training. An evolutionary scenario generation process is designed with a fitness evaluation methodology that integrates the processes of AI player modeling, simulation and model training based on artificial neural networks. The fitness function for scenario evaluation can be automatically constructed based on the proposed methodology. To further enhance the evaluation of scenarios, we specifically study the impact of the timing of events in a scenario and propose a generic scenario representation model that characterizes individual scenario based on the types and timing of events in the scenario. We present an extensive evaluation of our framework by validating our AI player model, demonstrating the impact of timing of events in a scenario and comparing the effectiveness of our data-driven framework with our previous heuristic-based approach and a random baseline. The results show that it is necessary to consider the timing of events for scenario evaluation and the proposed framework works well in generating scenarios for game-based training.

Design space exploration refers to the evaluation of implementation alternatives for many engineering and design problems. A popular exploration approach is to run a large number of simulations of the actual system with varying sets of configuration parameters to search for the optimal ones. Due to the potentially huge resource requirements, cloud-based simulation execution strategies should be considered in many cases. In this paper, we look at the issue of running large-scale simulation-based design space exploration problems on commercial Infrastructure-as-a-Service clouds, namely Amazon EC2, Microsoft Azure and Google Compute Engine. To efficiently manage cloud resources used for execution, the key problem would be to accurately predict the running time for each simulation instance in advance. This is not trivial due to the currently wide range of cloud resource types which offer varying levels of performance. In addition, the widespread use of virtualization techniques in most cloud providers often introduces unpredictable performance interference. In this paper, we propose a resource and application-aware (RA2) prediction approach to combat performance variability on clouds. In particular, we employ neural network based techniques coupled with non-intrusive monitoring of resource availability to obtain more accurate predictions. We conducted extensive experiments on commercial cloud platforms using an evacuation planning design problem over a month-long period. The results demonstrate that it is possible to predict simulation execution times in most cases with high accuracy. The experiments also provide some interesting insights on how we should run similar simulation problems on various commercially available clouds.

In this paper, we propose a role-dependent data-driven modeling approach to simulate pedestrians' motion in high density scenes. It is commonly observed that pedestrians behave quite differently when walking in dense crowd. Some people explore routes towards their destinations. Meanwhile, some people deliberately follow others, leading to lane formation. Based on these observations, two roles are included in the proposed model: leader and follower. The motion behaviors of leader and follower are modeled separately. Leaders' behaviors are learned from real crowd motion data using state-action pairs while followers' behaviors are calculated based on specific targets that are obtained dynamically during the simulation. The proposed role-dependent data-driven model is trained on crowd video data in one dataset and is then applied to two other different datasets to test its generality and effectiveness. The simulation results demonstrate that the proposed role-dependent data-driven model is capable of simulating crowd behaviors in crowded scenes realistically and reproducing collective crowd behaviors such as lane formation.

In this paper, a novel self-learning gene expression programming (GEP) methodology named SL-GEP is proposed to improve the search accuracy and efficiency of GEP. In contrast to the existing GEP variants, the proposed SL-GEP features a novel chromosome representation in which each chromosome is embedded with subfunctions that can be deployed to construct the final solution. As part of the chromosome, the subfunctions are self-learned or self-evolved by the proposed algorithm during the evolutionary search. By encompassing subfunctions or any partial solution as input arguments of another subfunction, the proposed SL-GEP facilitates the formation of sophisticated, higher-order, and constructive subfunctions that improve the accuracy and efficiency of the search. Further, a novel search mechanism based on differential evolution is proposed for the evolution of chromosomes in the SL-GEP. The proposed SL-GEP is simple, generic and has much fewer control parameters than the traditional GEP variants. The proposed SL-GEP is validated on 15 symbolic regression problems and six even-parity problems. Experimental results show that the proposed SL-GEP offers enhanced performances over several state-of-the-art algorithms in terms of accuracy and search efficiency.

Railway timetable scheduling is a fundamental operational problem in the railway industry and has significant influence on the quality of service provided by the transport system. This paper explores the periodic railway timetable scheduling (PRTS) problem, with the objective to minimize the average waiting time of the transfer passengers. Unlike traditional PRTS models that only involve service lines with fixed cycles, this paper presents a more flexible model by allowing the cycle of service lines and the number of transfer passengers to vary with the time period. An enhanced differential evolution (DE) algorithm with dual populations, termed “dual-population DE” (DP-DE), was developed to solve the PRTS problem, yielding high-quality solutions. In the DP-DE, two populations cooperate during the evolution; the first focuses on global search by adopting parameter settings and operators that help maintain population diversity, while the second one focuses on speeding up convergence by adopting parameter settings and operators that are good for local fine tuning. A novel bidirectional migration operator is proposed to share the search experience between the two populations. The proposed DP-DE has been applied to optimize the timetable of the Guangzhou Metro system in Mainland China and six artificial periodic railway systems. Two conventional deterministic algorithms and seven highly regarded evolutionary algorithms are used for comparison. The comparison results reveal that the performance of DP-PE is very promising.

This paper addresses a complicated problem in project management termed the payment scheduling negotiation problem. The problem is a practical extension of the classical multi-mode resource constrained project scheduling problem and it considers the financial aspects of both the project client and contractor in a contracting project. The client and contractor negotiate with each other to determine an optimal payment schedule and an activity schedule so as to maximize their net present values (NPVs). As the NPV of the client and the NPV of the contractor are conflicting objectives, this paper first formulates the PSNP as a bi-objective optimization problem. To solve this problem effectively, a non-dominated sorting genetic algorithm II (NSGA-II) approach is proposed. In the negotiation, the client and contractor may have two preferences: the ideal NPVs for the client and the contractor, and the optimization degree of the activity schedule. In order to tackle these preferences, this paper further introduces a new dominance relation named the extended r-dominance relation. The er-dominance relation extends the r-dominance relation and is able to deal with multiple preferences described by aspiration functions. Experimental results show that by incorporating the NSGA-II with the er-dominance, the proposed approach is promising for the PSNP.

Differential Evolution is a new paradigm of evolutionary algorithm which has been widely used to solve nonlinear and complex problems. The performance of DE is mainly dependent on the parameter settings, which relate to not only characteristics of the specific problem but also the evolution state of the algorithm. Hence, determining the suitable parameter settings of DE is a promising but challenging task. This paper presents an enhanced algorithm, namely, the stochastic coding differential evolution, to improve the robustness and efficiency of DE. Instead of encoding each individual as a vector of floating point numbers, the proposed SDE represents each individual by a multivariate normal distribution. In this way, individuals in the population can be more sensible to their surrounding regions and the algorithm can explore the search space region-by-region. In the SDE, a newly designed update operator and a random mutation operator are incorporated to improve the algorithm performance. Traditional DE operators such as the mutation scheme and the crossover operator are also accordingly extended. The proposed SDE has been validated by nine benchmark test functions with different characteristics. Five EAs are compared in the experiment study. The comparison results demonstrate the effectiveness and efficiency of the SDE.

In wireless sensor networks (WSNs), sensors near the sink can be burdened with a large amount of traffic, because they have to transmit data generated by themselves and those far away from the sink. Hence the sensors near the sink would deplete their energy much faster than the others, which results in a short network lifetime. Using mobile sink is an effective way to tackle this issue. This paper explores the problem of determining the optimal movements of the mobile sink to maximize the network lifetime. A novel ant colony optimization algorithm (ACO), namely the ACO-MSS, is developed to solve the problem. The proposed ACO-MSS takes advantage of the global search ability of ACO and adopts effective heuristic information to find a near globally optimal solution. Multiple practical factors such as the forbidden regions and the maximum moving distance of the sink are taken into account to facilitate the real applications. The proposed ACO-MSS is validated by a series of simulations on WSNs with different characteristics. The simulation results demonstrate the effectiveness of the proposed algorithms.

Scheduling sensor activities is an effective way to prolong the lifetime of wireless sensor networks (WSNs). In this paper, we explore the problem of wake-up scheduling in WSNs where sensors have different lifetime. A novel local wake-up scheduling (LWS) strategy is proposed to prolong the network lifetime with full coverage constraint. In the LWS strategy, sensors are divided into a first layer set and a successor set. The first layer set which satisfies the coverage constraint is activated at the beginning. Once an active sensor runs out of energy, some sensors in the successor set will be activated to satisfy the coverage constraint. Based on the LWS strategy, this paper presents an ant colony optimization based method, namely mc-ACO, to maximize the network lifetime. The mc-ACO is validated by performing simulations on WSNs with different characteristics. A recently published genetic algorithm based wake-up scheduling method and a greedy based method are used for comparison. Simulation results reveal that mc-ACO yields better performance than the two algorithms.

Direct search (DS) and evolutionary algorithms (EAs) are two of the most representative branches of derivative-free optimization methods. However, traditional DS becomes deficient in multimodal problems, while EAs suffer from long computational time due to the blind search caused by randomness in evolutionary operators. This paper proposes a new derivative-free optimization algorithm that addresses both the above issues, avoiding prematurity while maintaining fast convergence speed. The new algorithm first estimates basins of attractions in the search space by analyzing samples of the objective function. An adaptive exploitation method with the ability to predict promising search directions is then applied to search the estimated basins in parallel. The new algorithm is evaluated on both unimodal and multimodal benchmark functions. Experimental results show that the algorithm is a promising global optimizer with fast convergence speed.

Many real-world applications can be modeled as multi-objective optimization problems (MOPs). Applying differential evolution (DE) to MOPs is a promising research topic and has drawn a lot of attention in recent years. To search high-quality solutions for MOPs, this paper presents a robust adaptive DE (termed AS-MODE) with following two features. First, a stochastic coding strategy is used to improve the solution quality. This coding strategy represents each individual by a stochastic region, which enables the algorithm to fine-tune solutions efficiently. Second, a probability-based adaptive control strategy is utilized to reduce the influence of parameter settings. The adaptive control strategy associates each parameter with a candidate value set. Better candidate values would have higher selection probabilities to generate new individuals. The performance of the proposed AS-MODE is compared with several highly regarded multi-objective evolutionary algorithms. Simulation results on ten benchmark test functions with different characteristics reveal that AS-MODE yields very promising performance.

The automated synthesis and optimization of power electronic circuits (PECs) is a significant and challenging task in the field of power electronics. Traditional methods such as the gradient-based methods, the hill-climbing techniques and the genetic algorithms (GA), are either prone to local optima or not efficient enough to find highly accurate solutions for this problem. To better optimize the design of PECs, this paper presents an extended histogram-based estimation of distribution algorithm with an adaptive refinement process (EDA/a-r). In the EDA/a-r, the histogram-based estimation of distribution algorithm is used to roughly locate the global optimum, while the adaptive refinement process is used to improve the accuracy of solutions. The adaptive refinement process, with its search radius adjusted adaptively during the evolution, is executed to search the surrounding region of the best-so-far solution in every generation. To maintain the diversity, a historic learning strategy is used in constructing the probabilistic model and a mutation strategy is hybridized in the sampling operation. The proposed EDA/a-r has been successfully used to optimize the design of a buck regulator. Experimental results show that compared with the GA and the particle swarm optimization (PSO), the EDA/a-r can obtain much better mean solution quality and is less likely to be trapped into local optima.

This paper presents an enhanced Latin square genetic algorithm (LSGA). It makes the chromosomes to be more sensible to their surrounding regions. The algorithm applies orthogonal design method to every chromosome in the population to detect chromosomes with high fitness values in the surrounding regions. Orthogonal design method makes it more concise and direct to find the delegate to represent the situation of the surrounding regions. We execute the proposed algorithm to solve 15 test functions and compare it with traditional algorithm without using orthogonal design method. The results show that the proposed algorithm can find optimal or close-to-optimal solutions with higher speed and more accuracy.

Instead of having fixed px and pm, this paper presents the use of fuzzy logic to adaptively tune px and pm for optimization of power electronic circuits throughout the process. By applying the K-means algorithm, distribution of the population in the search space is clustered in each training generation. Inferences of px and pm are performed by a fuzzy-based system that fuzzifies the relative sizes of the clusters containing the best and worst chromosomes. The proposed adaptation method is applied to optimize a buck regulator that requires satisfying some static and dynamic requirements. The optimized circuit component values, the regulator's performance, and the convergence rate in the training are favorably compared with the GA's using fixed px and p.