Large language models (LLMs) have seen remarkable growth in recent years. To leverage convenient LLM cloud services, users are inevitably to upload their prompts. Additionally, for tasks such as translation, reading comprehension, and summarization, associated files or context are inherently needed, whether or not they contain user privacy information. Despite the rapid progress in LLM capabilities, research on preserving user privacy during inference has been relatively scarce. To this end, this paper conducts some exploratory research in this domain. Firstly, we show that (1) the embedding space of tokens is highly sparse, and (2) LLMs primarily function in the orthogonal subspace of embedding space, these two factors making privacy extremely vulnerable. Then, we analyze the structural characteristics of LLMs and design a distributed privacy-preserving inference paradigm which can effectively resist privacy attacks. Finally, we perform a thorough evaluation of the defended models on mainstream tasks and find that low-bit quantization techniques can be effectively combined with our inference paradigm, achieving a balance between privacy, utility, and runtime memory efficiency.

Federated multilabel learning enables the collaborative training of multilabel classification models while preserving client privacy. Existing federated multilabel learning methods either fail to effectively capture label correlations, which significantly affects model performance in scenarios where labels are interdependent, or increase the risk of client privacy leakage due to the transmission of unnecessary client data. To this end, we propose FML-DGCN, a federated multi-label learning approach based on dynamic graph convolutional networks. In the local training phase, a dynamic graph convolutional module is designed and employed to generate label representations specific to the input image, with parameters efficiently optimized by our designed correlation alignment loss. Within the module, we employ a fully connected layer-based network to merge static label embeddings and image features for computing dynamic label graphs, instead of leveraging complex attention-based networks, making it suitable for federated learning environments with limited computing resources. In the federated aggregation phase, clients transmit only model parameters, without sharing any additional information such as scene knowledge, thus lowering the risk of client privacy leakage. Experimental results on four typical multilabel image classification datasets demonstrate the superiority of our approach.

The Nearest-Better Network (NBN) is a powerful method to visualize sampled data for continuous optimization problems while preserving multiple landscape features. However, the calculation of NBN is very time-consuming, and the extension of the method to combinatorial optimization problems is challenging but very important for analyzing the algorithm’s behavior. This paper provides a straightforward theoretical derivation showing that the NBN network essentially functions as the maximum probability transition network for algorithms. This paper also presents an efficient NBN computation method with logarithmic linear time complexity to address the time-consuming issue. By applying this efficient NBN algorithm to the OneMax problem and the Traveling Salesman Problem (TSP), we have made several remarkable discoveries for the first time: The fitness landscape of OneMax exhibits neutrality, ruggedness, and modality features. The primary challenges of TSP problems are ruggedness, modality, and deception. Three state-of-the-art TSP algorithms (EAX, LKH, and NLKH) have limitations when addressing challenges related to modality and deception, respectively. LKH, based on local search operators, fails when there are deceptive solutions near global optima. EAX, which is based on a single population, can efficiently maintain diversity. However, when multiple attraction basins exist, EAX retains individuals within multiple basins simultaneously, reducing inter-basin interaction efficiency and leading to algorithm’s stagnation. NLKH improves over LKH by leveraging learned edge weights to increase the chance of reaching the global basin, but it remains vulnerable to deceptive funnels due to biased learning from underrepresented complex instances.

Dynamic Multimodal Optimization Problems (DMMOPs) demand algorithms capable of swiftly locating and tracking multiple optimal solutions over time. The primary challenge lies in controlling the population diversity to facilitate effective exploration, all within the limitation of computational resources between consecutive environmental changes. In this article, we study the utilization of density information derived from both current and historical populations to enhance exploration. First, for each active sub-population, we construct a density landscape based on the distribution of concurrently active sub-populations and establish dominance relationships between candidate solutions in the sub-population based on density and fitness values, directing this sub-population toward exploring low-density promising areas. Then, for each converged sub-population, we construct a density landscape based on the distribution of sub-populations that have historically become extinct, guiding the restart of this sub-population in low-density unexploited areas. Finally, we develop a comprehensive framework of Density-Assisted Evolutionary Algorithm (DAEA), which encompasses density-assisted search and restart, also combined with initialization. Moreover, we employ prediction and memory strategies to enhance the performance of DAEA in dynamic environments. Notably, the algorithm relies on an external monitor to detect environmental changes and trigger the dynamic response strategy. DAEA is tested on the CEC’2022 dynamic multimodal optimization benchmark suite and is compared against several state-of-the-art dynamic multimodal optimization algorithms. The experimental results demonstrate the competitiveness of DAEA in handling DMMOPs. Additionally, experimental results from the berth allocation problem further confirm the applicability of DAEA to real-world dynamic multimodal optimization tasks.

Many real-world optimization problems exhibit dynamic characteristics, posing significant challenges for traditional optimization methods. Evolutionary Dynamic Optimization Algorithms (EDOAs) have been developed to address these challenges by adapting to changing environments over time. However, the reproducibility and consistency of experimental results in the literature remain limited due to the lack of publicly available source codes and the complexity of accurately re-implementing algorithms and performance evaluation protocols. To support the community, we introduce E volutionary D ynamic O ptimization LAB oratory (EDOLAB), an open source MATLAB platform designed for both research and educational purposes. EDOLAB includes 27 EDOAs, four highly configurable benchmark generators, and a growing suite of performance indicators. The platform supports full parameter tuning, batch experiment management, parallel execution, and automated statistical comparisons—including rankings, significance testing, box plots, and performance trend visualizations over time. An educational application allows users to observe: (a) dynamic changes in a 2D problem landscape, (b) the movement of individuals in response to these changes, and (c) the ability of an algorithm to track moving optima. By providing an integrated environment for experimentation, benchmarking, and instructional use, EDOLAB promotes reproducibility, comparative analysis, and a deeper understanding of EDOAs in dynamic environments.

Crowdsourcing leverages the collective intelligence of the crowd to collect data and solve complex computational tasks. Driven by this paradigm, data can now be gathered more efficiently and at larger scales, thereby increasing the need for effective dimensionality reduction, which makes feature selection (FS) essential for efficient learning. In this paper, we refer to the problem in which multiple workers in a crowdsourcing environment collect data and concurrently optimize FS as the crowdsourcing feature selection (CFS) problem. In CFS, workers perform FS on local data and upload candidate feature subsets to complete the outsourced task. Nevertheless, the heterogeneity across multiple data sources hinders the formation of a reliable and high-quality consensus solution. To address this issue, we propose a cooperative learning-based Distributed Evolutionary Algorithm for the CFS problem (DEA-CFS). First, we formulate the CFS problem and define the roles of workers and the server, as well as their interactions in a crowdsourcing environment. Second, on the worker side, we design a distributed cooperative learning strategy that refines local solutions and mitigates data heterogeneity through confidence-aware fitness comparison. Third, on the server side, we introduce an adaptive credibility-based aggregation mechanism that aggregates a robust consensus solution. Extensive experiments on 18 datasets with up to 10,000 features demonstrate the efficiency and effectiveness of DEA-CFS. Specifically, compared to four existing distributed baselines, DEA-CFS achieves a superior average rank of 1.16 and obtains the best performance on 15 of the 18 datasets.

Most existing semi-supervised community detection algorithms leverage known communities to learn community structures, subsequently identifying communities that align with these learned community structures. However, differences in community structures may render the community structures learned by these methods inappropriate for the community containing the given node of interest. As a result, the identified community may exclude the given node or be of poor quality. Inspired by the success of reinforcement learning, we propose a Semi-supervised Local community detection method based on Reinforcement Learning, named SLRL, which only explores parts of the network surrounding the given node. It first extracts the local structure around a given node with an extractor, followed by selecting communities that are similar to this local structure to distill useful communities. These selected communities are employed to train the expander, which expands the community containing a given node. Experimental results demonstrate that SLRL outperforms state-of-the-art algorithms on five real-world datasets.

Multi-Agent Reinforcement Learning (MARL) has shown great success in many aspects. However, the cooperative policy trained by MARL is vulnerable to adversarial attacks towards agents' observations, which could cause immeasurable damage to the agent team. A few techniques have been developed to test the robustness of MARL, but the feasibility of real implementation is not carefully considered. In this work, we propose a two-step framework to conduct destructive and sparse attacks under realistic conditions. The first step contains two attack scenes: Ally Observation Attack (AOA) and Enemy Observation Attack (EOA), which have limitations on the use of agents' observations. The first step selects victim agent from the team and screens the attack time, while the second step generates adversarial perturbation and adds it to the observation of the chosen victim in a black-box environment. To the best of our knowledge, this is the first work to test the robustness with partial observation and conduct the test in a black-box environment. Experiments on SMAC environments demonstrate that our methods show great performance in reducing the win rate and the team reward of the agent team trained by QMIX algorithm with lower perturbation steps.

Knowledge Tracing (KT) is a fundamental task in educational data mining that mainly focuses on tracing students’ dynamic knowledge states of skills. Bayesian Knowledge Tracing (BKT) has been widely researched and applied due to its good interpretability, using the hidden Markov model to model students’ question–answering process. Standard BKT considers only one skill in each question. To address this limitation, we proposed a Multi-skills Bayesian Knowledge Tracing (MBKT) method based on evolutionary algorithms in our previous work. MBKT employs evolutionary algorithms as the optimization method for BKT, enabling it to trace changes in students’ mastery of multiple skills simultaneously. However, MBKT has the drawback of taking too long for a single individual evaluation, and a large number of valueless individuals invoke the real evaluation process, especially when dealing with a large amount of data to be evaluated. This hinders its application in real online education scenarios. Therefore, the Surrogate Model-assisted Multi-skills Bayesian Knowledge Tracing (SA-MBKT) method is proposed to address these issues by introducing a window strategy and a surrogate model method. Extensive experiments on real-world datasets demonstrate that SA-MBKT significantly enhances temporal performance without affecting the predictive performance of the model.

Gradient inversion attacks (GIAs) pose significant challenges to the privacy-preserving paradigm of distributed learning. These attacks employ carefully designed strategies to reconstruct victim’s private training data from their shared gradients. However, existing work mainly focuses on attacks and defenses for image-modal data, while the study for text-modal data remains scarce. Furthermore, the performance of the limited attack researches on text-modal data is also unsatisfactory, which can be partially attributed to the finer granularity of text data compared to image. To bridge the existing research gap, we propose a high-fidelity attack method tailored for Transformer-based language models (LMs). In our method, we initially reconstruct the label space of the victim’s training data by leveraging the characteristics of the Transformer architecture. After that, we propose a shallow-to-deep paradigm to facilitate gradient matching, which can significantly improve the attack performance. Furthermore, we develop a weighted surrogate loss that resolves the consistent deviation issue present in current attack researches. A substantial number of experiments on Transformer-based LMs (e.g., Bert and GPT) demonstrate that our attack is competitive and significantly outperforms existing methods. In the final part of this paper, we investigate the influence of the inherent position embedding module within the Transformer architecture on attack performance, and based on the analysis results, we propose a countermeasure to alleviate part of the privacy leakage issue in distributed learning.

The information available in multi-attributed road-social networks includes network structure, location information, and numerical attributes. Most studies mainly focus on mining communities by combining structure with attributes or structure with location, which do not consider structure, attributes, and location simultaneously. Therefore, we propose a parameter-free algorithm, called LCDMRS, to mine local communities in multi-attributed road-social networks. LCDMRS extracts a sub-network surrounding the given node and embeds it to generate the vector representations of nodes, which incorporates both structural and attributed information. Based on the vector representations of nodes, the average cosine similarity between nodes is designed to ensure both the structural and attributed cohesiveness of the community, while the community node density is designed to ensure the spatial cohesiveness of the community. Targeting the community node density and cosine similarity of nodes, LCDMRS takes the given node as the starting node and employs the community dominance relation to expand the community outward. Experimental results on multiple real-world datasets demonstrate LCDMRS outperforms comparison algorithms.

Distributed learning frameworks aim to train global models by sharing gradients among clients while preserving the data privacy of each individual client. However, extensive research has demonstrated that these learning frameworks do not absolutely ensure the privacy, as training data can be reconstructed from shared gradients. Nevertheless, the existing privacy-breaking attack methods have certain limitations. Some are applicable only to small models, while others can only recover images in small batch size and low resolutions, or with low fidelity. Furthermore, when there are some data with the same label in a training batch, existing attack methods usually perform poorly. In this work, we successfully address the limitations of existing attacks by two steps. Firstly, we model the coefficient of variation (CV) of features and design an evolutionary algorithm based on the minimum CV to accurately reconstruct the labels of all training data. After that, we propose a stepwise gradient inversion attack, which dynamically adapts the objective function, thereby effectively and rationally promoting the convergence of attack results towards an optimal solution. With these two steps, our method is able to recover high resolution images (224*224 pixel, from ImageNet and Web) with high fidelity in distributed learning scenarios involving complex models and larger batch size. Experiment results demonstrate the superiority of our approach, reveal the potential vulnerabilities of the distributed learning paradigm, and emphasize the necessity of developing more secure mechanisms. Source code is available at https://github.com/MiLab-HITSZ/2023YeHFGradInv.

Federated multi-label learning can collaboratively train multi-label classification models without compromising user privacy. Compared to multi-class learning, one of the most critical issues of multi-label learning is how to capture the correlations between labels, which is often ignored by existing research on federated multi-label learning. In this paper, a scene-based federated multi-label learning framework is proposed, which effectively utilizes the dependencies among labels for model training on the client-side and aggregates diverse client information on the server-side. Specifically, in the local training phase, a scene recognition module is employed to detect the scene for each image and the corresponding label co-occurrence matrix is used to guide the propagation of image features on the label graph. In the aggregation phase, a scene-aware aggregation method is adopted to enrich the scene-label co-occurrence information of each client. Experiments on PASCAL VOC 2007 and MS-COCO show that our proposed method can significantly improve the accuracy of federated multi-label image classification.

Deep Neural Networks (DNNs) are widely used for the outstanding performance in many fields. However, the training of DNN models has high requirements for the users’ data and computation resources, so many users with limited resources tend to download pre-trained models from some platforms and then finetune the pre-trained models to match their own tasks. However, the pre-trained models are under the threat of the backdoor attack. The backdoor attackers inject backdoors in the models, leading the backdoor models to predict target predictions designed by the attackers in advance. However, most existing backdoor model inspection methods rely on the clean data samples from the dataset of the model, which are difficult to get for users who just download the pre-trained models from the platforms. There are also a few defense methods not dependent on the data, but they also have their limits in practice. We propose Data-Free Masking and Reverse Engineering Loops (DF-MREL), a simple yet efficient data-free method for backdoor model inspection, which is widely applicable when resources are limited. Our experiments show its excellent performance in detecting backdoor models. Source code will be published after accepted.

Target tracking has broad applications like disaster relief, and unmanned aerial vehicles (UAVs) have been universally applied in target tracking in recent years. Due to the strong responsiveness to deceptive reward signals and diverse exploration, evolutionary reinforcement learning (ERL) is a more noteworthy option for training UAVs than common reinforcement learning. However, for ERL contains too many neural networks, its training efficiency is not satisfactory enough. To address this shortcoming, this paper proposes an evolutionary reinforcement learning with double replay buffers (ERLDRB) for UAV online target tracking problem. Firstly, considering the energy consumption and the possible delay of feedback signals to the UAV, a more realistic model of UAV online target tracking problem is designed. Then based on the problem formulation, ERLDRB utilizes a double experience replay buffers technique to increase learning efficiency in the training stage, which can better solve real-world UAV online target tracking problem. Simulation results show that ERLDRB outperforms multiple contrasting algorithms on the designed model.

Gradient inversion attacks (GIAs) have posed significant challenges to the emerging paradigm of distributed learning, which aims to reconstruct the private training data of clients (participating parties in distributed training) through the shared parameters. For counteracting GIAs, a large number of privacy-preserving methods for distributed learning scenario have emerged. However, these methods have significant limitations, either compromising the usability of global model or consuming substantial additional computational resources. Furthermore, despite the extensive efforts dedicated to defense methods, the underlying causes of data leakage in distributed learning still have not been thoroughly investigated. Therefore, this paper tries to reveal the potential reasons behind the successful implementation of existing GIAs, explore variations in the robustness of models against GIAs during the training process, and investigate the impact of different model structures on attack performance. After these explorations and analyses, this paper propose a plug-and-play GIAs defense method, which augments the training data by a designed vicinal distribution. Sufficient empirical experiments demonstrate that this easy-to-implement method can ensure the basic level of privacy without compromising the usability of global model.

Privacy-preserving machine learning requires that models do not reveal any private information about their training data. However, model inversion attacks (MIAs), which aim to recover the features of training data, pose a huge threat to the security of AI models. Most existing MIAs assume that the target model is white-box, but most models deployed in reality are black-box, and these models can only be accessed like an oracle. There are a few studies for black-box scenarios, but their performance is limited. In this paper, we firstly formulate the MIA problem completely in Bayesian perspective. Second, we propose a novel two-stage MIA approach, the Coarse-to-Fine Model Inversion Attack (C2FMI), which efficiently addresses the MIA problem in the black-box scenario. In stage I of C2FMI, we design a reverse network that constrains the recovered images (also named attacked images) to fall near the manifold space of the training data. In stage II, we design a black-box oriented strategy which further facilitates the attacked images to approach the training data. Empirically, C2FMI achieves a performance that even surpasses existing white-box attack methods. Furthermore, we design the stability analysis method for analyzing the stability of C2FMI along with existing MIAs. Finally, we explore the potential countermeasures which could defend against our attacks.

Model inversion attacks aim to reveal information about sensitive training data of AI models, which may lead to serious privacy leakage. However, existing attack methods have limitations in reconstructing training data with higher feature fidelity. In this paper, we propose an evolutionary model inversion attack approach (EvoMI) and empirically demonstrate that combined with the systematic search in the multi-degree-of-freedom latent space of the generative model, the simple use of an evolutionary algorithm can effectively improve the attack performance. Concretely, at first, we search for latent vectors which can generate images close to the attack target in the latent space with low-degree of freedom. Generally, the low-freedom constraint will reduce the probability of getting a local optima compared to existing methods that directly search for latent vectors in the high-freedom space. Consequently, we introduce a mutation operation to expand the search domain, thus further reduce the possibility of obtaining a local optima. Finally, we treat the searched latent vectors as the initial values of the post-processing and relax the constraint to further optimize the latent vectors in a higher-freedom space. Our proposed method is conceptually simple and easy to implement, yet it achieves substantial improvements and outperforms the state-of-the-art methods significantly.

The superior performance of large-scale pre-trained models, such as Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-trained Transformer (GPT), has received increasing attention in both academic and industrial research and has become one of the current research hotspots. A pre-trained model refers to a model trained on large-scale unlabeled data, whose purpose is to learn general language representation or features for fine-tuning or transfer learning in subsequent tasks. After pre-training is complete, a small amount of labeled data can be used to fine-tune the model for a specific task or domain. This two-stage method of “pre-training+fine-tuning” has achieved advanced results in natural language processing (NLP) tasks. Despite widespread adoption, existing fixed fine-tuning schemes that adapt well to one NLP task may perform inconsistently on other NLP tasks given that different tasks have different latent semantic structures. In this article, we explore the effectiveness of automatic fine-tuning pattern search for layer-wise learning rates from an evolutionary optimization perspective. Our goal is to use evolutionary algorithms to search for better task-dependent fine-tuning patterns for specific NLP tasks than typical fixed fine-tuning patterns. Experimental results on two real-world language benchmarks and three advanced pre-training language models show the effectiveness and generality of the proposed framework.

Local spatial-aware community detection refers to detecting a spatial-aware community for a given node using local information. A spatial-aware community means that nodes in the community are tightly connected in structure, and their locations are close to each other. Existing studies focus on detecting the local non-overlapping spatial-aware community, i.e., detecting a spatial-aware community containing the given node. However, many geosocial networks often contain overlapping spatial-aware communities. Therefore, we propose a local overlapping spatial-aware community detection (LOSCD) problem, which aims to detect all spatial-aware communities that contain a given node with local information. To address LOSCD problem, we design an algorithm based on Spatial Modularity and Edge Similarity, called SMES. SMES contains two processes: spatial expansion and structure detection. The spatial expansion process involves using spatial modularity to identify nodes that are spatially close, while the structural detection process employs edge similarity to identify nodes that are structurally close. Experimental results demonstrate that SMES outperforms comparison algorithms in terms of both structural and spatial cohesiveness.

Individuals are often involved in multiple online social networks. Considering that owners of these networks are unwilling to share their networks, some global algorithms combine information from multiple networks to detect all communities in multiple networks without sharing their edges. When data owners are only interested in the community containing a given node, it is unnecessary and computationally expensive for multiple networks to interact with each other to mine all communities. Moreover, data owners who are specifically looking for a community typically prefer to provide less data than the global algorithms require. Therefore, we propose the Local Collaborative Community Detection problem (LCCD). It exploits information from multiple networks to jointly detect the local community containing a given node without directly sharing edges between networks. To address the LCCD problem, we present a method developed from M method, called colM, to detect the local community in multiple networks. This method adopts secure multiparty computation protocols to protect each network’s private information. Our experiments were conducted on real-world and synthetic datasets. Experimental results show that colM method could effectively identify community structures and outperform comparison algorithms.

Owing to the lack of a universal definition of communities, some semi-supervised community detection approaches learn the concept of community structures from known communities, and then dig out communities using learned concepts of communities. In some cases, users are only interested in the community containing a given node. However, communities detected by these semi-supervised approaches may not contain a given node. Besides, these methods traverse the entire network to detect many communities and cost more resources than a local algorithm. Therefore, it is necessary and meaningful to find the local community that contains a given node with prior information on the local network around the given node. We call this a Semi-supervised Local Community Detection (SLCD) problem. In this paper, prior information refers to certain known communities. To address the SLCD problem, we propose the Semi-supervised Local community detection with the Structural Similarity algorithm, called SLSS, which uses some known communities instead of all known communities. The idea of SLSS is to use the structural similarity between the known communities and the detected community, calculated by the graph kernel, to guide the expansion of the community. Experimental results show that SLSS outperforms other algorithms on six real-world datasets.

The identification of the local community structure in geo-social networks has been gaining increasing attention. The structure of geo-social networks evolves over time with the addition/deletion of edges/nodes and the update of node locations, which has motivated recent studies to mine local communities in dynamic geo-social networks. Mining communities in evolving geo-social networks is essential for understanding the evolution of group behaviors. However, in most previous studies on the community mining in dynamic networks, local spatial-aware communities were not identified in evolving geo-social networks. Therefore, in this study, the problem of determining local spatial-aware communities in evolving geo-social networks is proposed. To address this problem, we propose a parameter-free algorithm, called LSADEN. Specifically, LSADEN involves two main steps: i) selecting candidate nodes, where LSADEN defines the community dominance relation under dynamic environments to obtain candidate nodes that improve the community in terms of the community quality or the smoothness between communities at adjacent time stamps; ii) community expansion, where LSADEN designs the Manhattan distance of communities to add some candidate nodes to the local community. Experimental results on six real-world datasets and one synthetic dataset show that LSADEN performs well both in terms of the quality of communities and the smoothness between communities at adjacent time stamps.

Nature-inspired population-based stochastic search algorithms (SSA) have demonstrated effectiveness in solving many real-world dynamic optimization problems (DOPs), such as dynamic optimal power flow (DOPF) problems. The basic idea of solving DOPs using SSAs is to “track the moving optima”, rather than solving the changed problems from scratch. Its hidden assumption is that the problems are slightly changed in general, and it is expected that the search process can be accelerated by learning from past search experiences. However, the hidden assumption that the current search process can always benefit from previous search experiences might not be true for certain cases. For example, if the current problem is not similar to any of its previous problems, the historical solutions might not be helpful for the current search. To reduce such negative transfer, the following issues are worthy of study: how to choose which historical problems to learn from, and how to determine the degree of learning from historical problems. To solve the above problems, we propose a dynamic processing framework based on fuzzy rules from the perspective of incorporating human knowledge for optima tracking. The experimental results on the DOPF problems show that the SSA with the proposed optima tracking strategy outperforms other comparative algorithms. We open up the code and data of our algorithm44https://github.com/DMiC-Lab-HFUT/SMDE-Transfer.

The generation of SAT instances is an important issue in computer science, and it is useful for researchers to verify the effectiveness of SAT solvers. Addressing this issue could inspire researchers to propose new search strategies. SAT problems exist in various real-world applications, some of which have more than one solution. However, although several algorithms for generating random SAT instances have been proposed, few can be used to generate hard instances that have multiple predefined solutions. In this paper, we propose the KHidden-M algorithm to generate SAT instances with multiple predefined solutions that could be hard to solve by the local search strategy when the number of predefined solutions is small enough and the Hamming distance between them is not less than half of the solution length. Specifically, first, we generate an SAT instance that is satisfied by all of the predefined solutions. Next, if the generated SAT instance does not satisfy the hardness condition, then a strategy will be conducted to adjust clauses through multiple iterations to improve the hardness of the whole instance. We propose three strategies to generate the SAT instance in the first part. The first strategy is called the random strategy, which randomly generates clauses that are satisfied by all of the predefined solutions. The other two strategies are called the estimating strategy and greedy strategy, and using them, we attempt to generate an instance that directly satisfies or is closer to the hardness condition for the local search strategy. We employ two SAT solvers (i.e., WalkSAT and Kissat) to investigate the hardness of the SAT instances generated by our algorithm in the experiments. The experimental results show the effectiveness of the random, estimating and greedy strategies. Compared to the state-of-the-art algorithm for generating SAT instances with predefined solutions, namely, M-hidden, our algorithm could be more effective in generating hard SAT instances.

The problem of finding the spatial-aware community for a given node has been defined and investigated in geosocial networks. However, existing studies suffer from two limitations: 1) the criteria of defining communities are determined by parameters, which are difficult to set, and 2) algorithms may require global information and are not suitable for situations where the network is incomplete. Therefore, we propose spatial-aware local community detection (SLCD), which finds the spatial-aware local community with only local information and defines the community based on the difference in terms of the sparseness of edges inside and outside the community. Specifically, to address the SLCD problem, we design a novel spatial aware local community detection algorithm based on dominance relation, but this algorithm incurs high cost. To further improve the efficiency, we propose a greedy algorithm. Experimental results demonstrate that the proposed greedy algorithm outperforms the comparison algorithms.

Cooperative coevolution (CC) is a paradigm equipped with the divide-and-conquer strategy for solving large-scale optimization problems (LSOPs). Currently, the computational resource allocation schemes of most CC could be divided into two categories, namely, equal allocation to all subproblems and preference allocation to the subproblems with a large contribution. However, the difficult subproblems are not carefully considered by the existing computational resource allocation schemes. For these subproblems, the investment of computational resources cannot quickly improve the fitness value, which leads to their small early contribution and being neglected. In this article, we comprehensively analyze the imbalanced nature of the subproblems from their difficulty and contribution in LSOPs. First, we propose a method to quantify the optimization difficulty of the problems during the evolution process, which considers both the difficulty of the fitness landscape and the behaviors of the optimization algorithm. Then, we propose a novel both difficulty and contribution-based CC framework, called DCCC, which encourages the allocation of the computational resources to more contributing and more difficult subproblems. DCCC is tested on the CEC’2010 and CEC’2013 large-scale optimization benchmarks, and is compared with several typical CC frameworks and state-of-the-art large-scale optimization algorithms. The experimental results demonstrate that DCCC is very competitive.

As a special class of multiobjective optimization problems (MOPs), multiparty multiobjective optimization prob-lems (MPMOPs) widely exist in real-world applications. In MPMOPs, there are multiple decision makers (DMs) concerning multiple different conflicting objectives. The goal of solving MPMOPs is to catch the best solutions satisfying all DMs as far as possible. To our best knowledge, there is little attention on solving MPMOPs, and only two optimization algorithms, i.e., OptMPNDS and OptMPNDS2, are proposed. These two algorithms are both based on non-dominated sorting genetic algorithm II (NSGA-II). However, there is no algorithm pro-posed from the decomposition perspective to solve MPMOPs. Multiobjective evolutionary algorithm based on decomposition (MOEA/D) is a popular multiobjective evolutionary optimization algorithm for MOPs. In this paper, we embed the party-by-party strategy into MOEA/D and propose the novel optimization algorithm MOEA/D-MP to solve MPMOPs. The experimental results on the benchmarks have demonstrated the effectiveness of MOEA/D-MP.

Gradient-based repair aims to repair infeasible solutions to feasible ones using the gradient information of the constraints. As an effective constraint handling method, gradientbased repair has received extensive attention and has been applied in various evolutionary algorithms (EAs). Nevertheless, due to the complexity of constraints in practical problems, a single infeasible solution often needs to be repaired multiple times until it becomes a feasible solution or reaches the maximum number of repairs. As far as we know, existing related research on gradient-based repair mainly applies this method directly to EAs, while there is little work in the evolutionary computing community on how to improve gradient-based repair. Currently, the multiple repairs for a single individual are independent. That is, the current repair does not consider the previous repair experience. However, only using gradient information to repair infeasible individuals may result in oscillations in the search process. Therefore, in this paper, we propose a heuristic gradient-based repair method (HGR) which exploits the previous repair information of an individual to alleviate this issue. Experimental results on several benchmarks demonstrate the effectiveness of the proposed method. The source code is available at https://github.com/DMiC-Lab-HFUT/HGR-SMC2022.

In real-world applications, each data owner might have only partial information of the complete social networks. They wish to find the community structure within multiple networks but without sharing their data directly. However, the existing works on collaborative community detection rarely consider the edges privacy issue in the networks. In this article, from the view of secure multiparty computation, we present two methods to detect the community structure of the multiple networks without directly exchanging edges’ information. These two methods are developed from the fast modularity algorithm ($fastModular$) and the label propagation algorithm (LPA), and they are called$CofastModular$and$CoLPA$, respectively. Both methods can detect the community structure within multiple networks without the need to directly exchange the edges’ information. Experiments are conducted on several real-world and synthetic networks. Experimental results show that$CofastModular$and$CoLPA$could identify community structure effectively.

Multimodal optimization problems (MMOPs) are common problems with multiple optimal solutions. In this article, a novel method of population division, called nearest-better-neighbor clustering (NBNC), is proposed, which can reduce the risk of more than one species locating the same peak. The key idea of NBNC is to construct the raw species by linking each individual to the better individual within the neighborhood, and the final species of the population is formulated by merging the dominated raw species. Furthermore, a novel algorithm is proposed called NBNC-PSO-ES, which combines the advantages of better exploration in particle swarm optimization (PSO) and stronger exploitation in the covariance matrix adaption evolution strategy (CMA-ES). For the purpose of demonstrating the performance of NBNC-PSO-ES, several state-of-the-art algorithms are adopted for comparisons and tested using typical benchmark problems. The experimental results show that NBNC-PSO-ES performs better than other algorithms.

Nearest-Better Clustering (NBC) is an emergent niching technique in Swarm and Evolutionary Computation for optimization, which does not need to fix the number or radius of clusters in advance. The key idea of NBC is to first link each individual to its nearest better neighbor to form a spanning tree of all individuals in the population, and then partition all individuals into clusters by deleting the longer edges in the spanning tree. In this paper, a survey on the Nearest-Better Clustering algorithms and applications in multimodal and dynamic optimization is provided. First, the basic NBC algorithm is introduced. Second, the improvements of the basic NBC are detailed. Third, multimodal and dynamic optimization algorithms powered by NBC are enlisted and discussed.

Studies have shown that deep neural networks (DNNs) are susceptible to adversarial attacks, which can cause misclassification. The adversarial attack problem can be regarded as an optimization problem, then the genetic algorithm (GA) that is problem-independent can naturally be designed to solve the optimization problem to generate effective adversarial examples. Considering the dimensionality curse in the image processing field, traditional genetic algorithms in high-dimensional problems often fall into local optima. Therefore, we propose a GA with multiple fitness functions (MF-GA). Specifically, we divide the evolution process into three stages, i.e., exploration stage, exploitation stage, and stable stage. Besides, different fitness functions are used for different stages, which could help the GA to jump away from the local optimum.Experiments are conducted on three datasets, and four classic algorithms as well as the basic GA are adopted for comparisons. Experimental results demonstrate that MF-GA is an effective black-box attack method. Furthermore, although MF-GA is a black-box attack method, experimental results demonstrate the performance of MF-GA under the black-box environments is competitive when comparing to four classic algorithms under the white-box attack environments. This shows that evolutionary algorithms have great potential in adversarial attacks.

Adversarial examples about deep learning models have been paid much attention in recent years, including single-label adversarial examples and multi-label adversarial examples. In this paper, for the first time, an empirical study of generating a multi-label adversarial example to hide all labels in a multi-label example is presented. The objective of hiding all labels in a multi-label example is to make deep learning models know nothing about the environments. That is very worthy of studying because deep learning models will say there is nothing, although the real input has more than one label. In the empirical study, we use five state-of-the-art multi-label attack algorithms, i.e., ML-CW, ML-DP, FGSM, MI-FGSM, MLA-LP, four popular datasets, i.e., VOC2007, VOC2012, NUS-WIDE and COCO, and two typical models ML-GCN and ASL for evaluation. We conduct extensive experiments and report the attack success rates, the amount of perturbations of the adversarial examples generated by state-of-the-art multi-label attack algorithms. We also report the attack performance when a typical defending algorithm based on JPEG compression is used. The work in this paper is beneficial to the future study of generating multi-label adversarial examples as well as defending them.

Although followership has been widely studied in sociology and management, the problem of finding followers has not drawn attention in the field of artificial intelligence. We refer to the problem of finding followers of a given object as followers search. In sociology, followers are close to their leaders, and leaders are superior to their followers. In this article, aimed at finding followers of a given object, we formulate followers on the basis of both superiority and closeness. The former means that the given object should be superior to followers, and the latter means that followers should be close to the given object. We present a followers search algorithm to find the followers of the given object. Furthermore, we apply the ideas of followers to the market basket and recommender system datasets. The experimental results demonstrate the rationality of the discovered followers on the market basket dataset and the improved performance of the TrustPMF algorithm by adopting followership on recommender system datasets, which indicate a promising future for followers search.

Multimodal optimization problems (MMOPs) are common in real-world applications and involve identifying multiple optimal solutions for decision makers to choose from. The core requirement for dealing with such problems is to balance the ability of exploration in the global space and exploitation in the multiple optimal areas. In this paper, based on the differential evolution (DE), we propose a novel algorithm focusing on the formulation, balance, and keypoint of species for MMOPs, called FBK-DE. First, nearest-better clustering (NBC) is used to divide the population into multiple species with minimum size limitations. Second, to avoid placing too many individuals into one species, a species balance strategy is proposed to adjust the size of each species. Third, two keypoint-based mutation operators named DE/keypoint/1 and DE/keypoint/2 are proposed to evolve each species together with traditional mutation operators. The experimental results of FBK-DE on 20 benchmark functions are compared with 15 state-of-the-art multimodal optimization algorithms. The comparisons show that the proposed FBK-DE performs competitively with these algorithms.

Large-scale optimization problems and constrained optimization problems have attracted considerable attention in the swarm and evolutionary intelligence communities and exemplify two common features of real problems, i.e., a large scale and constraint limitations. However, only a little work on solving large-scale continuous constrained optimization problems exists. Moreover, the types of benchmarks proposed for large-scale continuous constrained optimization algorithms are not comprehensive at present. In this article, first, a constraint-objective cooperative coevolution (COCC) framework is proposed for large-scale continuous constrained optimization problems, which is based on the dual nature of the objective and constraint functions: modular and imbalanced components. The COCC framework allocates the computing resources to different components according to the impact of objective values and constraint violations. Second, a benchmark for large-scale continuous constrained optimization is presented, which takes into account the modular nature, as well as both imbalanced and overlapping characteristics of components. Finally, three different evolutionary algorithms are embedded into the COCC framework for experiments, and the experimental results show that COCC performs competitively.

In real-world social networks, global information (e.g., the number of nodes and the connections between them) is incomplete or expensive to acquire; therefore, local community detection becomes especially important. Local community detection is used to identify the local community to which the given starting node belongs according to local information. For a given node, most existing local community detection methods can only find single scale local communities but not those of variable sizes. However, local communities with different scales are often required. Therefore, it is necessary and meaningful to find local communities of the given starting node with different scales; we call this multiscale local community detection. In this paper, we propose a new local modularity inspired by the global modularity and prove the equivalence of the proposed local modularity with two other typical local modularities. Furthermore, to detect local communities with different scales, we present a method based on the proposed local modularity. We test this method on several synthetic and real datasets, and the experimental results indicate that the detected community is meaningful and its scale can be changed reasonably.

Some real-world optimization problems involve multiple decision makers holding different positions, each of whom has multiple conflicting objectives. These problems are defined as multiparty multiobjective optimization problems (MPMOPs). Although evolutionary multiobjective optimization has been widely studied for many years, little attention has been paid to multiparty multiobjective optimization in the field of evolutionary computation. In this paper, a class of MPMOPs, that is, MPMOPs having common Pareto optimal solutions, is addressed. A benchmark for MPMOPs, obtained by modifying an existing dynamic multiobjective optimization benchmark, is provided, and a multiparty multiobjective evolutionary algorithm to find the common Pareto optimal set is proposed. The results of experiments conducted using the benchmark show that the proposed multiparty multiobjective evolutionary algorithm is effective.

Deep neural networks (DNNs) are used in various domains, such as image classification, natural language processing and face recognition, etc. However, the presence of malicious examples, generated by specific methods, could result in DNNs misclassification. Such maliciously modified examples are called adversarial examples. So far, most work about adversarial examples mainly focuses on the multi-class classification tasks, and only a little work has been done in the field of multi-label classification.In this study, we have proposed a novel algorithm that generates effective multi-label adversarial examples by solving a linear programming problem (MLA-LP). We minimize the l∞norm of distortion while constraining the changes in the label loss of the example after being perturbed. Then, we transform this constrained optimization problem into a linear programming problem for reducing the time cost. In comparison to the existing multi-label classification model attack algorithms, the attack performance of the proposed MLA-LP is found to be competitive, and the adversarial examples generated by MLA-LP have significantly smaller distortions.

Dynamic social networking has always been the focus of the social network research, and a large number of effective community detection algorithms have been developed. However, as the real social networks are difficult to access, it is also difficult to evaluate the effectiveness of the community detection algorithms on dynamic social networks. Existing dynamic social network generators only focus on edge or node changes, whereas the quality of the community structure (modularity) is not considered. We propose a time-evolving social network generator based on modularity (TESNG-M). In TESNG-M, according to the community partition of the original network, the evolutionary behavior is simulated by adding or deleting nodes and flipping edges so that a static social network with a specified modularity will be generated. By repeating the static generation process, we obtain a dynamic social network with a specified partition and modularity at each time step. Thus, the network generated by TESNG-M can effectively simulate a real dynamic social network and be used for community detection. Furthermore, the specified modularity of static synthetic networks and the dynamic modularity of dynamic synthetic networks could be regarded as the performance baseline of community detection algorithms in static and dynamic social networks, respectively.

Local community detection refers to finding the community that contains the given node based on local information, which becomes very meaningful when global information about the network is unavailable or expensive to acquire. Most studies on local community detection focus on finding non-overlapping communities. However, many real-world networks contain overlapping communities like social networks. Given an overlapping node that belongs to multiple communities, the problem is to find communities to which it belongs according to local information. We propose a framework for local overlapping community detection. The framework has three steps. First, find nodes in multiple communities to which the given node belongs. Second, select representative nodes from nodes obtained above, which tends to be in different communities. Third, discover the communities to which these representative nodes belong. In addition, to demonstrate the effectiveness of the framework, we implement six versions of this framework. Experimental results demonstrate that the six implementation versions outperform the other algorithms.

Considering the uncertainty of power generations, in addition to the uncertainty of loads, is more and more important because of the increasing use of renewable energy sources. Most existing works on dynamic optimal power flow (DOPF) have only focused on either the uncertainty of loads (called demand-side uncertainty) or the uncertainty of power generations (called supply-side uncertainty). As far as we know, only a little work on the dynamic OPF problems considered both uncertainties simultaneously. It might be because the combination of variable uncertainties could lead to a huge problem size for existing methods. In this paper, inspired by the ideas in the field of evolutionary dynamic optimization (EDO), we attempt to deal with uncertain parameters from the perspective of tracking the moving optimum. A species and memory enhanced differential evolutionary algorithm (called SMDE) is specially designed to solve the DOPF with double-sided uncertainties. Specifically, in order to deal with the double-sided uncertainties, a modified memory strategy and an improved multi-population strategy were introduced, where the multi-population strategy includes two versions. The experimental results on the modified IEEE 57-bus and 118-bus systems show that the proposed algorithms perform much better than the comparison algorithms for most cases.

In evolutionary multi-objective optimization, the results generated by an evolutionary algorithm usually contain an approximation, as good as possible, of the entire Pareto-optimal front. However, sometimes the number of Pareto-optimal solutions may be so large that the decision maker (DM) is incapable of manipulating or understanding them. Methods for considering only the Pareto-optimal solutions that the DM prefers indeed constitute a hot research topic in the evolutionary computation field. In this paper, we introduce a new dominance relation called $\hat g$-dominance, which is an improved version of the g-dominance relation and can be easily implemented in traditional multi-objective evolutionary algorithms. In this work, the proposed $\hat g$-dominance is implemented in NSGA-II. Our experimental results show the effectiveness of $\hat g$-NSGA-II with respect to the original g-NSGA-II.

Both Particle Swarm Optimization (PSO) and Evolution Strategy with Covariance Matrix Adaptation (CMA-ES) exhibit good performance when solving global optimization problems. However, PSO could be misled by historical information and falls into a local optimum. Further, CMA-ES cannot fully utilize global information. Therefore, in this paper, we first propose a time-window PSO (TW-PSO) as an improvement of PSO, which could enhance the exploration ability of the algorithm. Second, we design a hybrid algorithm of TW-PSO, PSO and CMA-ES, i.e., HTPC, which combines the advantages of TW-PSO, PSO, and CMA-ES. We test HTPC on single-objective optimization problems from the CEC-2019 100-Digit Challenge, and the experimental results show that the performance of HTPC is competitive.

Secure password storage is a vital aspect in systems based on password authentication, which is still the most widely used authentication technique, despite some security flaws. In this paper, we propose a password authentication framework that is designed for secure password storage and could be easily integrated into existing authentication systems. In our framework, first, the received plain password from a client is hashed through a cryptographic hash function (e.g., SHA-256). Then, the hashed password is converted into a negative password. Finally, the negative password is encrypted into an encrypted negative password (ENP) using a symmetric-key algorithm (e.g., AES), and multi-iteration encryption could be employed to further improve security. The cryptographic hash function and symmetric encryption make it difficult to crack passwords from ENPs. Moreover, there are lots of corresponding ENPs for a given plain password, which makes precomputation attacks (e.g., lookup table attack and rainbow table attack) infeasible. The algorithm complexity analyses and comparisons show that the ENP could resist lookup table attack and provide stronger password protection under dictionary attack. It is worth mentioning that the ENP does not introduce extra elements (e.g., salt); besides this, the ENP could still resist precomputation attacks. Most importantly, the ENP is the first password protection scheme that combines the cryptographic hash function, the negative password, and the symmetric-key algorithm, without the need for additional information except the plain password.

The negative survey could preserve the privacy of individuals when collecting sensitive information, which has received wide attention. The existing reconstruction methods of negative surveys are usually designed for the single question negative surveys. Although the reconstruction methods of single question negative surveys could also be applied to multiquestion negative surveys after minor modifications, the consistency between the joint distribution and the marginal distributions in the multiquestion reconstructed results has never been considered. In this paper, the concept of consistency in the multiquestion reconstructed results is defined. Then the consistency of the reconstructed results obtained by two typical reconstruction methods, i.e., NStoPS and NStoPS-I, is analyzed. Furthermore, a novel reconstruction method for multiquestion negative survey is proposed, which could return consistent and reasonable multiquestion reconstructed results. Finally, the simulated experimental results on collecting healthcare data also illustrate that the performance of our method is excellent in terms of accuracy.

Elements of a person's biometrics are typically stable over the duration of a lifetime, and thus, it is highly important to protect biometric data while supporting recognition (it is also called secure biometric recognition). However, the biometric data that are derived from a person usually vary slightly due to a variety of reasons, such as distortion during picture capture, and it is difficult to use traditional techniques, such as classical encryption algorithms, in secure biometric recognition. The negative database (NDB) is a new technique for privacy preservation. Reversing the NDB has been demonstrated to be an NP-hard problem, and several algorithms for generating hard-to-reverse NDBs have been proposed. In this paper, first, we propose negative iris recognition, which is a novel secure iris recognition scheme that is based on the NDB. We show that negative iris recognition supports several important strategies in iris recognition, e.g., shifting and masking. Next, we analyze the security and efficiency of negative iris recognition. Experimental results show that negative iris recognition is an effective and secure iris recognition scheme. Specifically, negative iris recognition can achieve a highly promising recognition performance (i.e., GAR = 98.94% at FAR = 0.01%, EER = 0.60%) on the typical database CASIA-IrisV3-Interval.

Most of the community detection methods require the global information of the original network to be available, however, it is often expensive (even no way) to obtain the global information of the network in many real-world networks. So, the local community detection, only based on the local information, becomes especially important. The local community is the community in the network to which a given starting node belongs. Some local community detection methods have been proposed. However, these methods did not consider the characteristics of the local community during the local community formation. In this paper, we analyze the formation of the local community and propose two local community detection algorithms based on the dynamic membership function. Each of the algorithms is divided into three stages: 1) the initial stage, 2) the middle stage, and 3) the closing stage. At the initial stage, we design a dynamical membership function to detect local community and nodes with the greatest neighborhood intersect rate could be added to the local community. At the middle stage, we design another dynamical membership function, and the goal of this stage is to make the connection of the node in the local community closest. At the closing stage, the third dynamical membership function is provided, and the local community is further improved by collecting some nodes that should not be omitted. We test our algorithms on several synthetic datasets and real datasets; the results show that the local communities detected by our method are closer to the real local communities.

Currently, many intelligent transportation systems (ITSs) require aggregation of location information. Although users enjoy the convenience of ITSs, privacy concerns have resulted in user's caution in offering location information. Certain studies have been performed to preserve user location privacy. However, most studies that focus on preserving location privacy require a trusted third party or do not consider the movements of users. In this paper, we propose a method based on negative surveys that can be used to estimate the number of people in geographic locations. This method, which can preserve user privacy regardless of user movements, adopts a simple negative survey algorithm for user devices and an estimation algorithm for the server and might be suitable for low-power mobile devices. The experimental results demonstrate that our method is capable of locating the people's gathering places with fine control granularity, which renders it a promising application.

Dynamic constrained optimization problems (DCOPs) are difficult to solve because both objective function and constraints can vary with time. Although DCOPs have drawn attention in recent years, little work has been performed to solve DCOPs with multiple dynamic feasible regions from the perspective of locating and tracking multiple feasible regions in parallel. Moreover, few benchmarks have been proposed to simulate the dynamics of multiple disconnected feasible regions. In this paper, first, the idea of tracking multiple feasible regions, originally proposed by Nguyen and Yao, is enhanced by specifically adopting multiple subpopulations. To this end, the dynamic species-based particle swam optimization (DSPSO), a representative multipopulation algorithm, is adopted. Second, an ensemble of locating and tracking feasible regions strategies is proposed to handle different types of dynamics in constraints. Third, two benchmarks are designed to simulate the DCOPs with dynamic constraints. The first benchmark, including two variants of G24 (called G24v and G24w), could control the size of feasible regions. The second benchmark, named moving feasible regions benchmark (MFRB), is highly configurable. The global optimum of MFRB is calculated mathematically for experimental comparisons. Experimental results on G24, G24v, G24w, and MFRB show that the DSPSO with the ensemble of strategies performs significantly better than the original DSPSO and other typical algorithms.

Clustering is a classical unsupervised learning task, which is aimed to divide a data set into several groups with similar objects. Clustering problem has been studied for many years, and many excellent clustering algorithms have been proposed. In this paper, we propose a novel clustering method based on density, which is simple but effective. The primary idea of the proposed method is given as follows. Firstly, the point with the largest local density in a cluster is considered as the cluster center. The local density of each point is estimated based on the distance (called radius) between the point and its k-th nearest neighbor. The point with a smaller radius indicates a larger local density. Secondly, the difference of the local densities between each two internal points should be small, while the difference between the density of a border point and the density of an internal point should be relatively large. Thirdly, if the intersection of k nearest neighbors of two points is small, they should be assigned to different clusters. The proposed algorithm has been compared with a typical clustering algorithm named FDPCluster, and the experimental results show that our algorithm has better clustering quality.

Evolutionary Algorithms (EAs) with gradient-based repair, which utilize the gradient information of the constraints set, have been proved to be effective. It is known that it would be time-consuming if all infeasible individuals are repaired. Therefore, so far the infeasible individuals to be repaired are randomly selected from the population and the strategy of choosing individuals to be repaired has not been studied yet. In this paper, the Species-based Repair Strategy (SRS) is proposed to select representative infeasible individuals instead of the random selection for gradient-based repair. The proposed SRS strategy has been applied to εDEag which repairs the random selected individuals using the gradient-based repair. The new algorithm is named SRS-εDEag. Experimental results show that SRS-εDEag outperforms εDEag in most benchmarks. Meanwhile, the number of repaired individuals is reduced markedly.

Evolutionary clustering is a hot research topic that clusters the time-stamped data and it is essential to some important applications such as data streams clustering and social network analysis. An evolutionary clustering should accurately reflect the current data at any time step while simultaneously not deviate too drastically from the recent past. In this paper, the differential evolution (DE) is applied to deal with the evolutionary clustering problem. Comparing with the typical k-means, evolutionary clustering based on DE (deEC) could perform a global search in the solution space. Experimental results over synthetic and real-world data sets demonstrate that the deEC provides robust and adaptive solutions.

Both multipopulation and memory are widely used approaches in the field of evolutionary dynamic optimization. It would be interesting to examine the effect of the combinations of multipopulation algorithms (MPAs) and memory schemes. However, since most of the existing memory schemes are proposed with single population algorithms, straightforwardly applying them to MPAs may cause problems. By addressing the possible problems, a new memory scheme is proposed for MPAs in this paper. In the experiments, several existing memory schemes and the newly proposed scheme are combined with a MPA, i.e. the Species-based Particle Swarm Optimizer (SPSO), and these combinations are tested on cyclic and acyclic problems. The experimental results indicate that 1) straightforwardly using the existing memory schemes sometimes degrades the performance of SPSO even on cyclic problems; 2) the newly proposed memory scheme is very competitive.

The Shortest Path (SP) problems are conventional combinatorial optimization problems. There are many deterministic algorithms for solving the shortest path problems in static topologies. However, in dynamic topologies, these deterministic algorithms are not efficient due to the necessity of restart. In this paper, an improved Genetic Algorithm (GA) with four local search operators for Dynamic Shortest Path (DSP) problems is proposed. The local search operators are inspired by Dijkstra's Algorithm and carried out when the topology changes to generate local shortest path trees, which are used to promote the performance of the individuals in the population. The experimental results show that the proposed algorithm could obtain the solutions which adapt to new environments rapidly and produce high-quality solutions after environmental changes.

In our previous work [1], the evolutionary repair technique has been introduced into the evolutionary design of the combinational logic circuits. In this paper, the evolutionary repair technique is improved, in which the number of the input vectors of the repair circuit is usually smaller than that of the corresponding incomplete circuit. The evolutionary algorithm with the improved evolutionary repair technique (i.e. erEDAII) is used to generate the polymorphic circuits. Experimental results demonstrate that some polymorphic circuits are evolved by erEDAII effectively. Especially, the polymorphic circuit with 8 inputs and 8 outputs could be evolved by the erEDAII.

Nowadays, data privacy has been widely concerned. The private set intersection means that several parties calculate the intersection of their private sets while without revealing extra information about their private data. The negative database (NDB) is a new technique for preserving privacy, and it stores information in the complementary set of a traditional database (DB). Reversing the NDB to recover the corresponding DB is an NP-hard problem, and this property is the security foundation of the NDB. Moreover, the NDB can directly support some database operations such as intersection, union, select and Cartesian product. However, so far, there is no research work about the secure multi-party computation based on NDBs. In this paper, firstly, a two-party private set intersection protocol based on NDBs is proposed, and its security and efficiency are analyzed. Then, the multi-party private set intersection protocol based on NDBs is given.

Evolutionary algorithms have been adopted to design logic circuits for many years. However, it takes too much time for evolutionary algorithms to generate circuits directly. Recently, the repair technique has been introduced into evolutionary design of the circuit to significantly decrease the time cost. And yet, the repair technique costs a lot of gate resource. In this paper, the evolutionary repair for evolutionary design of the combinational circuit is proposed, which generates the repair circuits with an evolutionary algorithm. The evolutionary repair could reduce the gate resource cost and does not spend much more time. The evolutionary repair is merged into the traditional evolutionary algorithm to form a novel evolutionary design algorithm, i.e. the erEDA. The experimental results demonstrate that the erEDA could balance the time cost and the gate resource consumption.

The scalability problem is hard to overcome in the evolutionary design of the logic circuits. In this paper, a hybrid algorithm of Generalized Disjunction Decomposition and Evolutionary Repair is proposed, which is also embedded with a novel technique for compressing outputs. This hybrid algorithm aims at evolving the relatively large combinational logic circuits automatically. The experimental results demonstrate that the hybrid algorithm could evolve the relatively large circuits successfully.

The negative database (NDB) is a complement of the corresponding database. The NDB could protect the privacy of the data, but it should be complete and hard-to-reverse. However, existent techniques cannot generate the complete and hard-to-reverse negative database. In this paper, a hybrid method is proposed to generate single negative databases. The proposed hybrid method includes two phases. Firstly, a complete negative database with a small size is generated by the transformation of the prefix algorithm. Secondly, a hard-to-reverse negative database, which is generated with the q-hidden method, is added into the small complete negative database. Therefore, the hybrid negative database is both complete and hard-to-reverse. Experiment results show that the NDB generated by the hybrid method is better than the NDB generated by the typical q-hidden method. Especially, the NDB generated by the q-hidden method can be reversed on average when the string length is 300. However, the NDB generated by the hybrid method cannot be reversed on average when the string length is 150.

Differential Evolution is one kind of Evolutionary Algorithms, which has been successfully applied to solve many optimization problems. In this paper, a directed differential mutation (DDM), which utilizes the directional derivative to decide a suitable search direction and a proper mutation step size, is proposed. It is merged into the classical DE to form a new algorithm, named directed differential evolution (DDE). The performance of the DDE is tested on 23 classical problems for numerical optimization. The experimental results demonstrate that the performance of the DDE outperforms the classical DE on most functions.

Polymorphic gates are special kinds of logic gates that can exhibit different functions under the control of environmental parameters, such as light, temperature, and VDD. These polymorphic gates can be used to build polymorphic circuits that perform different functions under different environments. Because polymorphic gates are different from traditional logic gates, the existent completeness theory for the traditional logic gate set is not suitable for the polymorphic gate set. So far, only the definition of the complete polymorphic gate set is given. There is no approach to judging whether a given polymorphic gate set is complete. The contributions of this article include three aspects. First, the impact of logic-1 and logic-0 on the completeness of the polymorphic gate set is discussed. Second, the theory and two related algorithms for judging the completeness of polymorphic gate sets with two modes are given. Finally, the theory and related algorithms for complete polymorphic gate sets with more than two modes are proposed.

In this paper, an eXtended Stepwise Dimension Reduction approach (XSDR) to evolutionary design of relatively large combinational logic circuits is proposed. In our previous work, a Stepwise Dimension Reduction approach (SDR) is introduced. The SDR divides a circuit into several layers. The layers are evolved one after another. However, some layers are difficult to be evolved. The XSDR improves the SDR by decomposing the original truth table of a layer to two truth tables. The new truth tables after decomposing are easy to be evolved. The proposed method has been tested with multipliers and the circuits taken from the Microelectronics Center of North Carolina (MCNC) benchmark library. The experimental results demonstrate that the XSDR extensively improves the performance of the SDR in terms of the number of fitness evaluations and the computational time.

The detector generation algorithm is the core of a negative selection algorithm (NSA). In most previous work, the NSAs generate the detector set randomly, which cannot guarantee to obtain an efficient detector set. To generate an approximately optimal detector set, in this paper, a novel detector generation algorithm for the real-valued negative selection algorithm (RNSA) is proposed. The proposed algorithm, named as the EvoSeedRNSA, adopts a genetic algorithm to evolve the random seeds to obtain an optimized detector set. The experimental results demonstrate that the EvoSeedRNSA has a better performance.

Evolutionary Negative Selection Algorithms (ENSAs) are proposed by combining negative selection model and evolutionary operators. In this paper, the convergence of ENSAs with two different mutation operators is analyzed. The first mutation operator is that only one bit of a detector is selected and flipped with a high probability. The second mutation operator is that every bit of a detector has a positive probability to be flipped. The analysis results show that the ENSAs with different mutation operators have different convergent properties. Especially, the shape of the self set will affect the convergence of ENSAs with the first mutation operator.

A novel self-adaption strategy for the parameter epsiv in epsiv-MOEA is proposed in this paper based on the analyses of the relationship between the value of epsiv and the maximum number of non-dominated solutions. Then this novel strategy is applied in epsiv-MOEA and tested on 10 common benchmark functions. The experimental results demonstrate that even if without the good initial value for the parameter s, epsiv-MOEA with this self-adaption strategy (named Algorithm 1) is able to approximately obtain the expected number of non-dominated solutions, which are very close to and uniformly distributed on the Pareto-optimal front. Furthermore, the genetic drift phenomenon in Algorithm 1 is discussed Two cases of genetic drift are pointed out, and one case can be fixed up by a simple approach proposed in this paper.

Inspired by biological immune principles, a novel Immune Genetic Programming based on Register-Stack structure (rs-IGP) is proposed in this paper. In rs-IGP, an antigen represents a problem to be solved, and an antibody represents a candidate solution. A flexible and efficient antibody representation based on register-stack structure is designed for rs-IGP. Three populations are adopted in rs-IGP, i.e. the common population, the elitist population and the self set. The immune genetic operators are also developed, including clone operator, recombination operator, mutation operator, hypermutation operator, crossover operator and negative selection operator. The experimental results demonstrate that rs-IGP has better performance.

Nature has been the source of inspiration for many machine learning algorithms. In particular, learning algorithms based on natural evolution have attracted much attention. Evolutionary computation...

The issues of the search biases selection based on stochastic ranking are pointed out by an example with three possible outputs and are also demonstrated by an experiment designed here. In order to improve the explicit search biases ability in feasible regions, three conditions for explicit search biases are presented and a novel search biases selection strategy with stochastic ranking is proposed in this paper. This strategy is applied to our new algorithm based on ES (evolution strategy). The new algorithm has been tested on 13 common benchmark functions and the experimental results have demonstrated that to some extent the convergence speed, the numerical accuracy and stability of best solutions are improved.

Using immune regulation mechanisms that include density regulation and network regulation, this paper proposes a novel immune genetic algorithm. Its core idea is that all individuals compose an antibody network, and it utilizes a density regulation mechanism to adjust individual diversity at an individual level and network regulation mechanism to achieve dynamic balance between individual diversity and population convergence. The dynamic regulative ability of this algorithm is analyzed and the approach to choosing the parameters is also given. As a novel adaptive resolving algorithm, it can be used to solve many complex optimization problems. This paper discusses solution of the frequency assignment problem and analyzes the parameters' influence upon the performance of this algorithm. The experimental results prove that this algorithm has good performance and can properly maintain the balance between individual diversity and population convergence.