Integrated circuits are increasingly sensitive to radiation-induced multi-node upset in advanced CMOS technology. This paper proposes a novel low-power quadruple-node-upset recovery latch (QNU-CPN), which is based on the feedback interconnection of twenty-two input-split C-elements with P-input and N-input (CPNs) to achieve high reliability. Post-layout simulation results for 45nm CMOS by HSPICE technology show that the proposed QNU-CPN latch exhibits a reduction in power consumption by an average of 56.45%, a reduction in power-delay product (PDP) by an average of 56.92%, a reduction in area-power-delay product (APDP) by an average of 58.59%, and a reduction in setup time by an average of 11.11%, in comparison to four other existing quadruple-node upset recovery latch (LDAVPM, QRHIL, QRHIL-LC, MURLAV). Furthermore, this paper proposes the recovery rate calculation algorithm method that can calculate the recovery rate based on the configuration of multiple fault-tolerant components.

Multi-objective evolutionary algorithms (MOEAs) have achieved notable success in recommendation systems (RSs) by meeting diverse user needs. However, existing MOEAs lack effective methods to coordinate the challenges of cold start, low convergence of multiple objectives and lack of explainable recommendation reasons. Therefore, we propose an enhancing multi-objective optimization recommendation algorithm by integrating large language models (named as MORA-LLM). MORA-LLM uses the vast knowledge, reasoning ability and natural language generation (NLG) ability of large language models (LLMs) to compensate for the shortcomings of MOEA-based RSs in semantic understanding. Firstly, an LLM-enhancing prediction score (LEPS) strategy is proposed to alleviate the cold start problem. LEPS obtains the user embedding vector by vast knowledge of LLM and extracts interaction information of similar users to improve the accuracy of prediction scores. Secondly, an LLM-enhancing search (LES) strategy is proposed to improve the convergence of the multi-objective. LES strategy combines the reasoning ability of LLM with the competitive idea of competitive swarm optimization to achieve efficient search and balance multiple objectives. Finally, the prediction scores are further corrected based on the LLM output results and MORA-LLM offers recommendation reasons to help users better understand the recommendation results. Experimental results on real-world datasets demonstrate that MORA-LLM significantly outperforms existing algorithms in terms of recommendation accuracy and convergence.

Evolutionary algorithms (EAs) have shown their competitiveness in solving feature selection (FS) problem. However, when facing high-dimensional data with a number of instances, there are two challenges for the existing EAs. (1) The increasing number of features causes the search space of EAs to grow exponentially, which is known as the “curse of dimensionality". (2) The increasing number of instances not only increases the evaluation cost of EAs, but also may degrade the quality of obtained feature subsets. To tackle the two challenges simultaneously, this paper proposes an instance selection (IS) assisted evolutionary FS algorithm, named ISA-EFS. In ISA-EFS, a complementary feature grouping strategy is first suggested, with which the search is performed on the feature group level instead of the single feature level, and the “curse of dimensionality" can be solved effectively. Based on the grouping strategy, two new evolutionary (grouping-oriented crossover and mutation) operators are designed, which achieve the feature subsets with good quality. Then, a novel instance selection algorithm is developed to select a small number of “representative" instances and used for high-dimensional feature selection (HDFS). In ISA-EFS, the suggested IS and FS algorithms are carried on alternately. Meanwhile, since IS is designed to assist FS, the computational resources are gradually removed from IS to FS, with which the quality of feature subsets obtained by ISA-EFS is continuously improved. Experimental results on 12 high-dimensional datasets with a number of instances demonstrate the effectiveness and efficiency of the proposed ISA-EFS, when compared with six state-of-the-art FS algorithms.

Subset selection is a fundamental problem across a wide range of applications. In this study, we explore scenarios where the variables within the original dataset are divided into distinct groups. Subsequently, we investigate an optimization problem that includes extra fairness constraints (i.e., partition matroid constraints), restricting the selection of a specified number of variables from each group, which is known as the fair subset selection (FSS) problem. First, for the case where the existing Pareto optimization algorithms do not have the ability to well handle the fairness constraints, due to they do not consider fairness constraints in the process of solutions generation. In this article, a Pareto Optimization algorithm named POFSS is proposed for FSS, by introducing a designed fairness balance flip operator. Also, we prove that POFSS has the approximation ability of$\max \{ {}[{\alpha }/{l}](1-e^{-\gamma }), {}({\gamma }/{k}), 1-e^{-r\overline {k}/k } \}$in polynomial time when$ l \lt \log _{2}{n}$and the probability of mutation is constant$c/n~(c\lt 2)$and this approximation ratio is no worse than the previous theoretical result ($\alpha $,$\gamma $represent two submodular ratios, and l is the number of disjoint groups). In addition, we apply POFSS to one typical FSS task named personalized recommendation (PR), where an acceleration strategy is designed and the acceleration ratio is strictly proved. Finally, the experimental results on the PR task show the proposed POFSS outperforms the state-of-the-art methods in addressing the FSS.

The abuse of community detection algorithms may bring the risk of privacy leakage. To protect personal privacy in complex networks, community detection attack algorithms are proposed, which can hide the true community structure of the whole network from the community detection algorithms by adding and deleting subtle edges. However, most of the existing algorithms perform attack based on a community structure so that a specific community detection method is usually adopted for obtaining the communities, which causes the algorithms to not perform well when the attacked community detection algorithm is unknown. To this end, a local structure-based community detection attack heuristic approach (LSHA) is proposed in this article, where the local structures, including several nodes with dense connections instead of the whole community structures, are considered. Unlike the whole community structures obtained by different community detection algorithms, which are usually different, the nodes in such a local structure are often assigned into the same community so that the attack is more general for different community detection algorithms. Specifically, in LSHA, a local structure selection strategy is proposed to maximize the attack effect, which selects two local structures for rewiring attack. Furthermore, two metrics, i.e., edge vulnerability and node entropy, are also suggested to select the nodes and edges for attack. In the experiments, the proposed LSHA is compared with five state-of-the-art attack algorithms. The experimental results against five representative community detection algorithms on nine real-world networks show that the proposed algorithm LSHA achieves good performance on both the attack effectiveness and the efficiency.

Multiobjective evolutionary algorithms (MOEAs) have been widely used in community detection in recent years. However, most of the existing MOEA-based ones adopted the same search strategies for all nodes and ignored the differences between the nodes. In fact, the nodes in a complex network have different structural characteristics and are of different importance during the search process of the community detection problem. To this end, in this article, a node classification-based search scheme is first proposed, where different kinds of nodes are searched in different ways. To be specific, the nodes in the network are classified into two types of nodes, candidate central (CC) nodes and noncentral (NC) nodes, by mapping the nodes into a structural similarity-based embedding space. The CC nodes are likely to be the centers of communities, and the rough structure can be searched quickly through activating the CC nodes. Then, the NC nodes are assigned to the communities with the activated central nodes. Based on the proposed scheme, a node classification-based MOEA named NCMOEA is then proposed. In NCMOEA, a mixed representation is designed to effectively encode the two different kinds of nodes. In addition, corresponding genetic operators are then suggested to search the two categories of nodes in different ways. Furthermore, an initialization strategy is also designed for initializing the population with high quality and good diversity. The experimental results on 15 real-world networks and several synthetic networks demonstrate the superiority of the proposed NCMOEA over nine representative algorithms for community detection.

Subset selection that selects a limited number of variables optimizing many given criteria, is a fundamental problem with various applications such as sparse regression and unsupervised feature selection. Among the existing algorithms for subset selection, evolutionary algorithms (EAs) have achieved great performance in obtaining the subsets with high quality. However, as the selected subsets are sparse, i.e., most variables of these solutions are zero, most existing EAs for subset selection problems pose great challenges to find the optimal solutions effectively and efficiently, especially when the size of original set is large. To this end, we propose the SPESS approach which is a sparsity preserved evolutionary algorithm for subset selection. To be specific, we design two sparsity preserved operators (i.e., sparsity preserved crossover operator and sparsity preserved mutation operator), which can be used in SPESS to ensure the sparsity of the generated solutions in the process of Pareto optimization. The main advantages of the proposed sparsity preserved operators are that they can obtain high quality solutions and improve the search efficiency of the algorithm. According to the experimental results on 12 sparse regression problems and 12 unsupervised feature selection problems, the proposed SPESS is superior over the state-of-the-arts in solving subset selection tasks and the effectiveness of the proposed two sparsity preserved operators is also verified.

Graph Convolutional Networks (GCNs) are widely used for skeleton-based action recognition and achieved remarkable performance. Due to the locality of graph convolution, GCNs can only utilize short-range node dependencies but fail to model long-range node relationships. In addition, existing graph convolution based methods normally use a uniform skeleton topology for all frames, which limits the ability of feature learning. To address these issues, we present the Graph Convolution Network with Self-Attention (SelfGCN), which consists of a mixing features across self-attention and graph convolution (MFSG) module and a temporal-specific spatial self-attention (TSSA) module. The MFSG module models local and global relationships between joints by executing graph convolution and self-attention branches in parallel. Its bi-directional interactive learning strategy utilizes complementary clues in the channel dimensions and the spatial dimensions across both of these branches. The TSSA module uses self-attention to learn the spatial relationships between joints of each frame in a skeleton sequence. It also models the unique spatial features of the single frames. We conduct extensive experiments on three popular benchmark datasets, NTU RGB+D, NTU RGB+D120, and Northwestern-UCLA. The results of the experiment demonstrate that our method achieves or exceeds the record accuracies on all three benchmarks. Our project website is available at https://github.com/SunPengP/SelfGCN.

Evolutionary algorithms (EAs) have shown their competitiveness in solving the problem of feature selection (FS). However, in most of the existing EA-based FS methods, one bit in the individual only represents one feature, which means with the number of features increasing, the search space of these methods increases exponentially and makes them not suitable for the data classification with high dimensions. To tackle the issue, in this article, a variable granularity search-based multiobjective EA, termed as VGS-MOEA, is proposed for high-dimensional FS, where one bit in the individual representation denotes a group of features and results in the search space reducing greatly. To be specific, at the beginning, the search granularity of VGS-MOEA is coarse (a bit denotes a great number of features), which helps the proposed algorithm detect the potentially good feature subsets quickly. As the evolution continues, the search granularity is refined gradually, where a bit denotes a smaller number of features until it only represents one feature. With this decomposition of granularity, a more refined search is performed and leads to the VGS-MOEA obtaining feature subsets with higher quality. Experimental results on 12 high-dimensional data sets with different characteristics have shown that in comparison with the state of the arts, the proposed VGS-MOEA has demonstrated its superiority in terms of the classification accuracy, the number of selected features, and the running time.

Recently, evolutionary algorithms (EAs) have shown their promising performance in solving the hyperspectral endmember extraction (EE) task. Despite that, most of the existing EA-based EE algorithms mainly take advantage of the global search capability of evolutionary computation. A few of them focus on the hyperspectral EE task itself, which is a sparse large-scale problem with constraint. To fill the gap, in this article, a global-to-local EA (GL-EA) is proposed, where the global and local search is performed sequentially to extract the endmembers effectively. Specifically, in the first global search stage, two complementary solution generation strategies, including asymmetric flip-based solution generation and spectral angle distance (SAD)-based solution repair, are designed, with which the sparse large-scale search space of hyperspectral EE is fully explored and the endmembers that satisfy the constraint could be achieved. Then, in the second stage, a perturbation-based local search is suggested, which further enhances the quality of the obtained endmembers. In addition, an endmember repetition-based solution selection strategy is also developed for both global and local search stages, by using which good solutions can be selected effectively during the evolution. Experimental results on different hyperspectral datasets demonstrate that when compared with the state-of-the-art EE algorithms, the proposed GL-EA could extract the endmembers with higher quality.

Hyperspectral images (HSI) contain hundreds of bands, which provide a wealth of spectral information and enable better characterization of features. However, the excessive dimensions and redundant information also cause a dimensional disaster for subsequent processing. Band selection is a widely-used dimension reduction technique for hyperspectral images. Traditional methods mainly consider the hyperspectral band selection problem at the level of data, and maintain the information contained in the data, without considering the spatial structures inside hyperspectral images. To fill the gap, in this work, an unsupervised hyperspectral band selection method through structure-conserved and neighborhood-grouped evolutionary algorithm (SNEA) is proposed. Different from other evolutionary algorithms for hyperspectral band selection, firstly, two spatial-structure related optimization objectives are designed, including the locally spatial structure denoted by the pixel’s spatial consistency with its adjacent neighbors and the globally spatial structure denoted by the affinity graph among pixels. With the designed objectives, the hyperspectral band selection is formulated as the problem of conserving spatial structures. Moreover, a neighborhood-grouped pair-wise learning strategy is proposed to generate high-quality offsprings. In this novel strategy, a neighborhood grouping operation is developed to divide the band space into several groups. The population can be initialized efficiently and the offspring solutions can be generated pairwisely under the guidance of grouping. Compared with 9 state-of-the-art comparison algorithms, experimental results on 3 standard hyperspectral datasets demonstrate that the band subset obtained by our proposed SNEA has a better classification performance than the comparison algorithms.

P2P lending is an increasingly prosperous financial market, where lenders can directly bid and invest on the loans posted by borrowers. However, when facing massive loan requests, it is very difficult and also boring for lenders to choose loan portfolios meeting their ideal expectations. Actually, when choosing loans, most lenders pursue the highest profit with the lowest risk as well as satisfying their hobbies. In this article, we formalize a multi-objective optimization problem to help lenders select loan portfolios. Specifically, the recommending scenario in P2P lending is formulated as a multi-objective optimization problem, where two objective functions are designed for capturing lenders’ multiple demands. On this basis, a multi-objective evolutionary algorithm based on return-risk management named MOEA-RRM is then proposed for the multi-objective optimization problem, which can help lenders choose loan portfolios to meet lenders’ multiple demands. Furthermore, in MOEA-RRM, a decision space dimensionality reduction strategy and an initialization strategy are proposed to improve the performance of algorithm. Finally, experimental results on a real-world P2P lending data set demonstrate the effectiveness of our proposed MOEA-RRM, i.e., the proposed approach can recommend the loan portfolio with a good trade-off between risk and return as well as satisfying the hobbies of lenders.

In real-world applications, there exist a lot of multiobjective optimization problems whose Pareto-optimal solutions are sparse, that is, most variables of these solutions are 0. Generally, many sparse multiobjective optimization problems (SMOPs) contain a large number of variables, which pose grand challenges for evolutionary algorithms to find the optimal solutions efficiently. To address the curse of dimensionality, this article proposes an evolutionary algorithm for solving large-scale SMOPs, which aims to mine the sparse distribution of the Pareto-optimal solutions and, thus, considerably reduces the search space. More specifically, the proposed algorithm suggests an evolutionary pattern mining approach to detect the maximum and minimum candidate sets of the nonzero variables in the Pareto-optimal solutions, and uses them to limit the dimensions in generating offspring solutions. For further performance enhancement, a binary crossover operator and a binary mutation operator are designed to ensure the sparsity of solutions. According to the results on eight benchmark problems and four real-world problems, the proposed algorithm is superior over existing evolutionary algorithms in solving large-scale SMOPs.

In recent years, multiobjective evolutionary algorithms (MOEAs) have been demonstrated to show promising performance in feature selection (FS) tasks. However, designing an MOEA for high-dimensional FS is more challenging due to the curse of dimensionality. To address this problem, in this article, a steering-matrix-based multiobjective evolutionary algorithm, called SM-MOEA, is proposed. In SM-MOEA, a steering matrix is suggested and harnessed to guide the evolution of the population, which not only improves the search efficiency greatly but also obtains the feature subsets with high quality. Specifically, each element SM (i, j) in the steering matrix SM reflects the probability of the j th feature that is selected in the i th individual (feature subset), which is generated by considering the importance of both the feature j and the individual i . Based on the suggested steering matrix, two important operators referred to as dimensionality reduction and individual repairing operators are developed to effectively steer the population evolution in each generation. In addition, an effective initialization and update strategy for the steering matrix is also designed to further improve the performance of SM-MOEA. The experimental results on 12 high-dimensional datasets with the number of features ranging from 3000 to 13 000 demonstrate the superiority of the proposed algorithm over several state-of-the-art algorithms (including single-objective and MOEAs for high-dimensional FS) in terms of both the number and quality of the selected features.

In recent decades, recommender systems have been well studied and widely applied. However, most recommenders unilaterally optimize the results from the buying customers’ views without considering expectations of other participants, e.g., merchants. Unfortunately, the expectations of customers and merchants in recommendation are different or even conflicted. Especially for popular group-trading markets, customers and merchants are competing in trading, i.e., customers want to meet their preferences or obtain gains with personal favorite items, while merchants want to recommend wholesale items with setting group-trading terms or conditions. In addition, some practical constraints are not fully considered by prior systems. In this article, we propose a cooperative–competitive evolutionary algorithm (i.e., CoEA) for the bidirectional recommendations in group-trading markets. Specifically, we, respectively, formalize two subproblems with designed objectives for two-sided participants in markets, and integrate the cooperative–competitive optimizations into one framework. Second, CoEA designs abinary encoding matrixfor individual representation to integrate the two subproblems. Furthermore, by assembling game evolution process, CoEA designs cooperative–competitive evolution operators, i.e., thecooperative crossoverandcompetitive mutation, which guide the solutions to equilibrium by, respectively, bridging communication between two populations of subproblems and optimizing distinctive objective in each population. Finally, we construct two real applications involving bidirectional recommendations, i.e., the group buying and P2P lending, and conduct extensive experiments with the real-world datasets. By comparing CoEA with several representative recommendation algorithms and evolutionary algorithms, the experimental results clearly demonstrate the effectiveness of CoEA.

As the size of available networks is continuously increasing (even with millions of nodes), large-scale complex networks are receiving significant attention. While existing overlapping-community detection algorithms are quite effective in analyzing complex networks, most of these algorithms suffer from scalability issues when applied to large-scale complex networks, which can have more than 1,000,000 nodes. To address this problem, we propose an efficient local-expansion-based overlapping-community detection algorithm using local-neighborhood information (OCLN). During the iterative expansion process, only neighbors of nodes added in the last iteration (rather than all neighbors) are considered to determine whether they can join the community. This significantly reduces the computational cost and enhances the scalability for community detection in large-scale networks. A belonging coefficient is also proposed in OCLN to filter out incorrectly identified nodes. Theoretical analysis demonstrates that the computational complexity of the proposed OCLN is linear with respect to the size of the network to be detected. Experiments on large-scale LFR benchmark and real-world networks indicate the effectiveness of OCLN for overlapping-community detection in large-scale networks, in terms of both computational efficiency and detected-community quality.

Community detection has been recognized as one of the most important tools to discover useful information hidden in complex networks which is usually hard to be obtained by simple observations. Existing community detection algorithms have demonstrated their effectiveness on a variety of complex networks, most of them, however, suffer from the scalability issue on complex networks without a clear community structure due to the challenge in the detection of ambiguous community structure. To address this issue, in this paper, we propose a community structure enhancement method, termed CSE, for community detection in complex networks. In the proposed CSE, the community structure of a network is enhanced by adding links between the nodes possibly belonging to the same community and reducing links between those belonging to different communities, thereby converting an ambiguous community structure into a structure much clearer than the original one. The experimental results show the superior performance of the proposed CSE over five state-of-the-art community detection algorithms on both synthetic benchmark networks and real-world networks, especially for those without a clear community structure.

As a frequently-used dimensionality reduction technique in machine learning, feature selection has attracted interests in the last decade. Since feature selection is essentially a combinatorial optimization problem, how to search the valuable feature subset is a challenging optimization task. Particle swarm optimization (PSO) algorithm and its variations have shown their competitiveness in solving feature selection problem. However, they have been proven to be easily trapped into the local optimal in high-dimensional space due to their intrinsic characteristic of quick convergence. To this end, an effective binary particle swarm optimization algorithm, named CBPSOFS, is proposed for feature selection, where a community-grouping based adaptive updating strategy is designed to avoid trapping into the local optimum and enhance the performance of PSO algorithm in feature selection. To be specific, the correlationship among features is used to construct the feature network, where multiple feature groups are obtained by dividing the achieved feature network. Considering that a community usually contains multiple similar features, the proposed adaptive updating strategy utilizes these feature groups to make the similar features not be included in the same particle so as to maintain the diversity of the population in the evolution. In addition, an information gain based initialization strategy and a history information based resetting strategy are also developed to improve the quality of obtained feature subset. Experimental results on several real world datasets have demonstrated the effectiveness of CBPSOFS in feature selection when compared with the several state-of-the-art baselines.

Influence maximization refers to selecting a group of nodes from a social network, which obtains the largest influence spread under a cascade model. However, most of the existing works only focused on the influence and ignored the diversity of influenced crowd. Thus, scholars have raised the issue of diversified social influence maximization recently, using the category information of nodes to design diversity indicator and introducing a trade-off parameter to balance the two objectives influence and diversity as one single objective for optimization. In fact, the category information of nodes in the network is usually difficult to be collected, thus the definition of diversity based on nodes' categories is not very general and accurate. In addition, it is very difficult to set the trade-off parameter, especially when there is no prior knowledge in real applications. To this end, we employ overlapping community structure information to design the diversity of nodes without any node's additional (e.g. category) information. Due to the two objectives of influence and diversity may be conflicting, a multi-objective evolutionary algorithm named MOEA-DIM is proposed to optimize the two objectives simultaneously, which does not need to set the tradeoff parameter between the two objectives. In MOEA-DIM, a network reduction strategy based on overlapping community structure is suggested to greatly reduce the search space. In addition, a population initialization strategy based on random walk is designed to accelerate the convergence of the algorithm. Experiments on six real-world datasets show that the proposed algorithm MOEA-DIM has promising performance in terms of both effectiveness and efficiency.

Crowdfunding is an emerging Internet fundraising platform in which creators post descriptions of their projects and investors glance over these projects to support or not. With the increasing amount of projects posted in the crowdfunding platform, it is necessary to develop personalized recommendation systems (RSs) by suggesting suitable projects to crowdfunding investors. In this paper, we propose a personalized recommender system for crowdfunding platform to make accurate, high profitable and diverse project recommendations for crowdfunding investors. Specifically, the task of personalized recommendation for crowdfunding platform is modeled as a multi-objective optimization problem. The proposed model maximizes two conflicting performance metrics named as utility-accuracy and topic-diversity. The utility-accuracy is obtained by the probabilistic spreading method, while the topic-diversity is evaluated by recommendation coverage. Then, a multi-objective evolutionary algorithm for personalized recommendation in crowdfunding platform (termed as MOEA-PRCP) is proposed for the two-objective optimization problem. In MOEA-PRCP, a novel initialization strategy is designed for speeding the convergence of the proposed algorithm. Extensive experiments are conducted on a real-world crowdfunding data collected from Indiegogo.com, and the experimental results clearly demonstrate the effectiveness of MOEA-PRCP for personalized recommendation in crowdfunding platform.

During the past two decades, a variety of multiobjective evolutionary algorithms (MOEAs) have been proposed in the literature. As pointed out in some recent studies, however, the performance of an MOEA can strongly depend on the Pareto front shape of the problem to be solved, whereas most existing MOEAs show poor versatility on problems with different shapes of Pareto fronts. To address this issue, we propose an MOEA based on an enhanced inverted generational distance indicator, in which an adaptation method is suggested to adjust a set of reference points based on the indicator contributions of candidate solutions in an external archive. Our experimental results demonstrate that the proposed algorithm is versatile for solving problems with various types of Pareto fronts, outperforming several state-of-the-art evolutionary algorithms for multiobjective and many-objective optimization.