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The Confluence of Evolutionary Computation and Multi-Agent Systems:A Survey 认领 引用 被引量：2
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作者 Tai-You Chen Wei-Neng Chen +5 位作者 Feng-Feng Wei Xiao-Qi Guo Wen-Xiang Song Rui Zhu Qiuzhen Lin Jun Zhang 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2025年第11期2175-2193,共19页
Both evolutionary computation(EC)and multiagent systems(MAS)study the emergence of intelligence through the interaction and cooperation of a group of individuals.EC focuses on solving various complex optimization prob... Both evolutionary computation(EC)and multiagent systems(MAS)study the emergence of intelligence through the interaction and cooperation of a group of individuals.EC focuses on solving various complex optimization problems,while MAS provides a flexible model for distributed artificial intelligence.Since their group interaction mechanisms can be borrowed from each other,many studies have attempted to combine EC and MAS.With the rapid development of the Internet of Things,the confluence of EC and MAS has become more and more important,and related articles have shown a continuously growing trend during the last decades.In this survey,we first elaborate on the mutual assistance of EC and MAS from two aspects,agent-based EC and EC-assisted MAS.Agent-based EC aims to introduce characteristics of MAS into EC to improve the performance and parallelism of EC,while EC-assisted MAS aims to use EC to better solve optimization problems in MAS.Furthermore,we review studies that combine the cooperation mechanisms of EC and MAS,which greatly leverage the strengths of both sides.A description framework is built to elaborate existing studies.Promising future research directions are also discussed in conjunction with emerging technologies and real-world applications. 展开更多
关键词 Distributed artificial intelligence distributed optimization evolutionary computation(EC) multi-agent systems(MAS)
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Gait Optimization of a Quadruped Robot Using Evolutionary Computation 认领 引用 被引量：5
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作者 Jihoon Kim Dang Xuan Ba +1 位作者 Hoyeon Yeom Joonbum Bae 《Journal of Bionic Engineering》 SCIE EI CSCD 2021年第2期306-318,共13页
Evolutionary Computation(EC)has strengths in terms of computation for gait optimization.However,conventional evolutionary algorithms use typical gait parameters such as step length and swing height,which limit the tra... Evolutionary Computation(EC)has strengths in terms of computation for gait optimization.However,conventional evolutionary algorithms use typical gait parameters such as step length and swing height,which limit the trajectory deformation for optimization of the foot trajectory.Furthermore,the quantitative index of fitness convergence is insufficient.In this paper,we perform gait optimization of a quadruped robot using foot placement perturbation based on EC.The proposed algorithm has an atypical solution search range,which is generated by independent manipulation of each placement that forms the foot trajectory.A convergence index is also introduced to prevent premature cessation of learning.The conventional algorithm and the proposed algorithm are applied to a quadruped robot;walking performances are then compared by gait simulation.Although the two algorithms exhibit similar computation rates,the proposed algorithm shows better fitness and a wider search range.The evolutionary tendency of the walking trajectory is analyzed using the optimized results,and the findings provide insight into reliable leg trajectory design. 展开更多
关键词 bionic robot evolutionary computation genetic algorithm gait optimization parameter perturbation convergence index
Coordinated Route Planning via Nash Equilibrium and Evolutionary Computation 认领 引用 被引量：10
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作者 严平 丁明跃 郑昌文 《Chinese Journal of Aeronautics》 EI CAS 2006年第1期18-23,共6页
The coordinated route planning problem for multiple unmanned air vehicles （UAVs）, a cooperative optimization problem, also a non-cooperative game, is addressed in the framework of game theory, A Nash equilibrium bas... The coordinated route planning problem for multiple unmanned air vehicles （UAVs）, a cooperative optimization problem, also a non-cooperative game, is addressed in the framework of game theory, A Nash equilibrium based route planner is proposed. The rational is that the structure of UAV subteam usually provides some inherent and implicit preference information, which help to find the optimum coordinated routes and the optimum combination of the various objective functions. The route planner combines the concepts of evolutionary computation with problem-specific chromosome structures and evolutionary operators and handles different kinds of mission constraints in hierarchical style. Cooperation and competition among UAVs are reflected by the definition of fitness function. Simulations validate the feasibility and superiority of the game-theoretic coordinated routes planner. 展开更多
关键词 route planning game theory UAV team evolutionary computation
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Evolutionary Computation for Large-scale Multi-objective Optimization: A Decade of Progresses 认领 引用 被引量：10
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作者 Wen-Jing Hong Peng Yang Ke Tang 《International Journal of Automation and computing》 CSCD 2021年第2期155-169,共15页
Large-scale multi-objective optimization problems(MOPs)that involve a large number of decision variables,have emerged from many real-world applications.While evolutionary algorithms(EAs)have been widely acknowledged a... Large-scale multi-objective optimization problems(MOPs)that involve a large number of decision variables,have emerged from many real-world applications.While evolutionary algorithms(EAs)have been widely acknowledged as a mainstream method for MOPs,most research progress and successful applications of EAs have been restricted to MOPs with small-scale decision variables.More recently,it has been reported that traditional multi-objective EAs(MOEAs)suffer severe deterioration with the increase of decision variables.As a result,and motivated by the emergence of real-world large-scale MOPs,investigation of MOEAs in this aspect has attracted much more attention in the past decade.This paper reviews the progress of evolutionary computation for large-scale multi-objective optimization from two angles.From the key difficulties of the large-scale MOPs,the scalability analysis is discussed by focusing on the performance of existing MOEAs and the challenges induced by the increase of the number of decision variables.From the perspective of methodology,the large-scale MOEAs are categorized into three classes and introduced respectively:divide and conquer based,dimensionality reduction based and enhanced search-based approaches.Several future research directions are also discussed. 展开更多
关键词 Large-scale multi-objective optimization high-dimensional search space evolutionary computation evolutionary algorithms scalability
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Evolutionary Computation Based Optimization of Image Zernike Moments Shape Feature Vector 认领 引用 被引量：1
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作者 LIU Maofu HU Hujun +2 位作者 ZHONG Ming HE Yanxiang HE Fazhi 《Wuhan University Journal of Natural Sciences》 CAS 2008年第2期153-158,共6页
The image shape feature can be described by the image Zernike moments. In this paper, we points out the problem that the high dimension image Zernike moments shape feature vector can describe more detail of the origin... The image shape feature can be described by the image Zernike moments. In this paper, we points out the problem that the high dimension image Zernike moments shape feature vector can describe more detail of the original image but has too many elements making trouble for the next image analysis phases. Then the low dimension image Zernike moments shape feature vector should be improved and optimized to describe more detail of the original image. So the optimization algorithm based on evolutionary computation is designed and implemented in this paper to solve this problem. The experimental results demonstrate the feasibility of the optimization algorithm. 展开更多
关键词 Zernike moment image Zernike moments shape feature vector image reconstruction evolutionary computation
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Evolutionary computation in China: A literature survey 认领 引用 被引量：2
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作者 Maoguo Gong Shanfeng Wang +2 位作者 Wenfeng Liu Jianan Yan Licheng Jiao 《CAAI Transactions on Intelligence Technology》 2016年第4期334-354,共21页
Evolutionary computation （EC） has received significant attention in China during the last two decades. In this paper, we present an overview of the current state of this rapidly growing field in China. Chinese resea... Evolutionary computation （EC） has received significant attention in China during the last two decades. In this paper, we present an overview of the current state of this rapidly growing field in China. Chinese research in theoretical foundations of EC, EC-based optimization, EC-based data mining, and EC-based real-world applications are summarized. 展开更多
关键词 Evolutionary computation Evolutionary algorithms Optimization Data mining
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A New Algorithm for On-Line Handwriting Signature Verification Based on Evolutionary Computation 认领 引用 被引量：7
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作者 ZHENG Jianbin ZHU Guangxi 《Wuhan University Journal of Natural Sciences》 EI CAS 2006年第3期596-600,共5页
The paper proposes an on-line signature verification algorithm, through which test sample and template signatures can be optimizedly matched, based on evolutionary computation （EC）. Firstly, the similarity of signat... The paper proposes an on-line signature verification algorithm, through which test sample and template signatures can be optimizedly matched, based on evolutionary computation （EC）. Firstly, the similarity of signature curve segment is defined, and shift and scale transforms are also introduced due to the randoness of on-line signature. Secondly, this paper puts forward signature verification matching algorithm after establishment of the mathematical model. Thirdly, the concrete realization of the algorithm based on EC is discussed as well. In addition, the influence of shift and scale on the matching result is fully considered in the algorithm. Finally, a computation example is given, and the matching results between the test sample curve and the template signature curve are analyzed in detail, The preliminary experiments reveal that the type of signature verification problem can be solved by EC. 展开更多
关键词 on-line signature signature verification evolutionary computation
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Evolutionary Computation in Social Propagation over Complex Networks: A Survey 认领 引用 被引量：2
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作者 Tian-Fang Zhao Wei-Neng Chen +1 位作者 Xin-Xin Ma Xiao-Kun Wu 《International Journal of Automation and computing》 CSCD 2021年第4期503-520,共18页
Social propagation denotes the spread phenomena directly correlated to the human world and society, which includes but is not limited to the diffusion of human epidemics, human-made malicious viruses, fake news, socia... Social propagation denotes the spread phenomena directly correlated to the human world and society, which includes but is not limited to the diffusion of human epidemics, human-made malicious viruses, fake news, social innovation, viral marketing, etc. Simulation and optimization are two major themes in social propagation, where network-based simulation helps to analyze and understand the social contagion, and problem-oriented optimization is devoted to contain or improve the infection results. Though there have been many models and optimization techniques, the matter of concern is that the increasing complexity and scales of propagation processes continuously refresh the former conclusions. Recently, evolutionary computation(EC) shows its potential in alleviating the concerns by introducing an evolving and developing perspective. With this insight, this paper intends to develop a comprehensive view of how EC takes effect in social propagation. Taxonomy is provided for classifying the propagation problems, and the applications of EC in solving these problems are reviewed. Furthermore, some open issues of social propagation and the potential applications of EC are discussed.This paper contributes to recognizing the problems in application-oriented EC design and paves the way for the development of evolving propagation dynamics. 展开更多
关键词 Evolutionary computation complex network propagation dynamics social diffusion evolution model optimization algorithm
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Using Evolutionary Computation to Solve Problems in Nonparametric Regression 认领 引用 被引量：2
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作者 Ding Lixin Kang Lishan +1 位作者 Chen Yuping Pan Zhengjun 《Wuhan University Journal of Natural Sciences》 EI CAS 1998年第1期27-31,共5页
This paper studies evolutionary mechanism of parameter selection in the construction of weight function for Nearest Neighbour Estimate in nonparametric regression. Construct an algorithm which adaptively evolves fine ... This paper studies evolutionary mechanism of parameter selection in the construction of weight function for Nearest Neighbour Estimate in nonparametric regression. Construct an algorithm which adaptively evolves fine weight and makes good prediction about unknown points. The numerical experiments indicate that this method is effective. It is a meaningful discussion about practicability of nonparametric regression and methodology of adaptive model-building. 展开更多
关键词 nonparametric regression Nearest Neighbour Estimate evolutionary computation nonhomogeneous selection adaptive model-building
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Orbit Design for Responsive Space Using Multiple-objective Evolutionary Computation 认领 引用
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作者 FU Xiaofeng WU Meiping ZHANG Jing 《空间科学学报》 CAS CSCD 北大核心 2012年第2期238-244,共7页
Responsive orbits have exhibited advantages in emergencies for their excellent responsiveness and coverage to targets.Generally,there are several conflicting metrics to trade in the orbit design for responsive space.A... Responsive orbits have exhibited advantages in emergencies for their excellent responsiveness and coverage to targets.Generally,there are several conflicting metrics to trade in the orbit design for responsive space.A special multiple-objective genetic algorithm,namely the Nondominated Sorting Genetic AlgorithmⅡ(NSGAⅡ),is used to design responsive orbits.This algorithm has considered the conflicting metrics of orbits to achieve the optimal solution,including the orbital elements and launch programs of responsive vehicles.Low-Earth fast access orbits and low-Earth repeat coverage orbits,two subtypes of responsive orbits,can be designed using NSGAI under given metric tradeoffs,number of vehicles,and launch mode.By selecting the optimal solution from the obtained Pareto fronts,a designer can process the metric tradeoffs conveniently in orbit design.Recurring to the flexibility of the algorithm,the NSGAI promotes the responsive orbit design further. 展开更多
关键词 Multiple-objective evolutionary computation Non-dominated Sorting Genetic AlgorithmⅡ(NSGAⅡ) Low-Earth Fast Access Orbit(FAO) Low-Earth Repeat Coverage Orbit(RCO) Successive-coverage constellation for responsive deployment
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Evolutionary Computation for Image Feature Extraction 认领 引用
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作者 Radunovic Ljubisa Shuo-zhong Wang 《Advances in Manufacturing》 EI CAS 2000年第4期295-298,共4页
Modifications to an image feature extraction approach involving evolutionary computation and autonomous agents are proposed. The described algorithm allows extraction of features with certain specified characteristics... Modifications to an image feature extraction approach involving evolutionary computation and autonomous agents are proposed. The described algorithm allows extraction of features with certain specified characteristics, while omitting other undesirable details in the image. Experimental results are presented with remarks. 展开更多
关键词 evolutionary computation autonomous agent replacement policy fitness stimulus
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Modeling and Simulation for High Energy Sub-Nuclear Interactions Using Evolutionary Computation Technique 认领 引用
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作者 Mahmoud Y. El-Bakry El-Sayed A. El-Dahshan +2 位作者 Amr Radi Mohamed Tantawy Moaaz A. Moussa 《Journal of Applied Mathematics and Physics》 2016年第1期53-65,共13页
High energy sub-nuclear interactions are a good tool to dive deeply in the core of the particles to recognize their structures and the forces governed. The current article focuses on using one of the evolutionary comp... High energy sub-nuclear interactions are a good tool to dive deeply in the core of the particles to recognize their structures and the forces governed. The current article focuses on using one of the evolutionary computation techniques, the so-called genetic programming (GP), to model the hadron nucleus (h-A) interactions through discovering functions. In this article, GP is used to simulate the rapidity distribution  of total charged, positive and negative pions for p--Ar and p--Xe interactions at 200 GeV/c and charged particles for p-pb collision at 5.02 TeV. We have done so many runs to select the best runs of the GP program and finally obtained the rapidity distribution  as a function of the lab momentum , mass number (A) and the number of particles per unit solid angle (Y). In all cases studied, we compared our seven discovered functions produced by GP technique with the corresponding experimental data and the excellent matching was so clear. 展开更多
关键词 Modeling Simulation Evolutionary Computation Genetic Programming Hadron-Nucleus Interaction Rapidity Distribution
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The Applications of Evolutionary Computation in Software Reliability 认领 引用
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作者 Youbing Wang Hao Sun and Lishan Kang 《Wuhan University Journal of Natural Sciences》 CAS 1996年第Z1期645-650,共6页
Software reliability models(SRMs) are the theoretic foundation of software reliability. However, the existence of intrinsic limitation of the preposition in traditional model building confines the applications of SRMs... Software reliability models(SRMs) are the theoretic foundation of software reliability. However, the existence of intrinsic limitation of the preposition in traditional model building confines the applications of SRMs. In this paper, a new method,evolutionary computation,is used to estimate parameters of SRMs .At the same time, new algorithms are also proposed and employed to build SRMs. As the experiment results demonstrate, evolutionary computation method is po'verful and effective. 展开更多
关键词 evolutionary computation software reliability model parameters estimationmodel building
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Complex Evolutionary Games:Problem,Method,and Future 认领 引用
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作者 Zhou-Zhi Lu Yi Jiang +4 位作者 Zhi-Fan Tang Liu-Yue Luo Chun-Hua Chen Jun Zhang Zhi-Hui Zhan 《Artificial Intelligence Science and Engineering》 2026年第2期141-164,共24页
Evolutionary game theory(EGT)is a complex and emerging research area.In recent years,the rapid development of evolutionary games has driven extensive research across multiple interdisciplinary domains.However,existing... Evolutionary game theory(EGT)is a complex and emerging research area.In recent years,the rapid development of evolutionary games has driven extensive research across multiple interdisciplinary domains.However,existing surveys remain scenario-specific and lack a comprehensive overview of complex evolutionary games.To address this research gap,this survey aims to provide a comprehensive review of existing studies in the field of complex evolutionary games,thereby offering a clear reference framework for future researchers.First,a novel 5C taxonomy framework is proposed to systematically characterize the core complexities in complex evolutionary games from five complementary dimensions,namely complex structures,contingent strategies,changing environments,coupled players,and compound payoffs.This framework provides a unified taxonomy for organizing fragmented studies and revealing the intrinsic relationships among different categories of complex evolutionary games.Second,based on the proposed 5C taxonomy framework,this survey systematically investigates and analyzes state-of-the-art studies,comprehensively summarizing their advantages and limitations.Finally,potential future research directions are discussed with the aim of promoting further advancements in complex evolutionary games.This survey seeks to provide researchers with a comprehensive reference and valuable research insights,thereby facilitating the continued development and prosperity of the evolutionary games field. 展开更多
关键词 evolutionary game theory complex evolutionary games 5C taxonomy framework complex systems evolutionary dynamics evolutionary computation
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Evolutionary Computation for Expensive Optimization:A Survey 认领 引用 被引量：18
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作者 Jian-Yu Li Zhi-Hui Zhan Jun Zhang 《Machine Intelligence Research》 EI CSCD 2022年第1期3-23,共21页
Expensive optimization problem(EOP) widely exists in various significant real-world applications. However, EOP requires expensive or even unaffordable costs for evaluating candidate solutions, which is expensive for t... Expensive optimization problem(EOP) widely exists in various significant real-world applications. However, EOP requires expensive or even unaffordable costs for evaluating candidate solutions, which is expensive for the algorithm to find a satisfactory solution. Moreover, due to the fast-growing application demands in the economy and society, such as the emergence of the smart cities, the internet of things, and the big data era, solving EOP more efficiently has become increasingly essential in various fields, which poses great challenges on the problem-solving ability of optimization approach for EOP. Among various optimization approaches, evolutionary computation(EC) is a promising global optimization tool widely used for solving EOP efficiently in the past decades. Given the fruitful advancements of EC for EOP, it is essential to review these advancements in order to synthesize and give previous research experiences and references to aid the development of relevant research fields and real-world applications. Motivated by this, this paper aims to provide a comprehensive survey to show why and how EC can solve EOP efficiently. For this aim, this paper firstly analyzes the total optimization cost of EC in solving EOP. Then, based on the analysis, three promising research directions are pointed out for solving EOP, which are problem approximation and substitution, algorithm design and enhancement, and parallel and distributed computation. Note that, to the best of our knowledge, this paper is the first that outlines the possible directions for efficiently solving EOP by analyzing the total expensive cost. Based on this, existing works are reviewed comprehensively via a taxonomy with four parts, including the above three research directions and the real-world application part. Moreover, some future research directions are also discussed in this paper. It is believed that such a survey can attract attention, encourage discussions, and stimulate new EC research ideas for solving EOP and related real-world applications more efficiently. 展开更多
关键词 Expensive optimization problem evolutionary computation evolutionary algorithm swarm intelligence particle swarm optimization differential evolution
Recent Advances in Evolutionary Computation 认领 引用 被引量：30
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作者 姚新 徐永 《Journal of Computer Science & Technology》 SCIE EI 2006年第1期1-18,共18页
Evolutionary computation has experienced a tremendous growth in the last decade in both theoretical analyses and industrial applications. Its scope has evolved beyond its original meaning of ＂biological evolution＂ t... Evolutionary computation has experienced a tremendous growth in the last decade in both theoretical analyses and industrial applications. Its scope has evolved beyond its original meaning of ＂biological evolution＂ toward a wide variety of nature inspired computational algorithms and techniques, including evolutionary, neural, ecological, social and economical computation, etc, in a unified framework. Many research topics in evolutionary computation nowadays are not necessarily ＂evolutionary＂. This paper provides an overview of some recent advances in evolutionary computation that have been made in CERCIA at the University of Birmingham, UK. It covers a wide range of topics in optimization, learning and design using evolutionary approaches and techniques, and theoretical results in the computational time complexity of evolutionary algorithms. Some issues related to future development of evolutionary computation are also discussed. 展开更多
关键词 evolutionary computation neural network ensemble prisoner＇s dilemma real-world application computational time complexity
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Regularized machine learning through constraint swarm and evolutionary computation applied to regression problems 认领 引用 被引量：1
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作者 Ahmad Mozaffari Nasser Lashgarian Azad Alireza Fathi 《International Journal of Intelligent Computing and Cybernetics》 EI 2014年第4期346-381,共36页
Purpose–The purpose of this paper is to demonstrate the applicability of swarm and evolutionary techniques for regularized machine learning.Generally,by defining a proper penalty function,regularization laws are embe... Purpose–The purpose of this paper is to demonstrate the applicability of swarm and evolutionary techniques for regularized machine learning.Generally,by defining a proper penalty function,regularization laws are embedded into the structure of common least square solutions to increase the numerical stability,sparsity,accuracy and robustness of regression weights.Several regularization techniques have been proposed so far which have their own advantages and disadvantages.Several efforts have been made to find fast and accurate deterministic solvers to handle those regularization techniques.However,the proposed numerical and deterministic approaches need certain knowledge of mathematical programming,and also do not guarantee the global optimality of the obtained solution.In this research,the authors propose the use of constraint swarm and evolutionary techniques to cope with demanding requirements of regularized extreme learning machine(ELM).Design/methodology/approach–To implement the required tools for comparative numerical study,three steps are taken.The considered algorithms contain both classical and swarm and evolutionary approaches.For the classical regularization techniques,Lasso regularization,Tikhonov regularization,cascade Lasso-Tikhonov regularization,and elastic net are considered.For swarm and evolutionary-based regularization,an efficient constraint handling technique known as self-adaptive penalty function constraint handling is considered,and its algorithmic structure is modified so that it can efficiently perform the regularized learning.Several well-known metaheuristics are considered to check the generalization capability of the proposed scheme.To test the efficacy of the proposed constraint evolutionary-based regularization technique,a wide range of regression problems are used.Besides,the proposed framework is applied to a real-life identification problem,i.e.identifying the dominant factors affecting the hydrocarbon emissions of an automotive engine,for further assurance on the performance of the proposed scheme.Findings–Through extensive numerical study,it is observed that the proposed scheme can be easily used for regularized machine learning.It is indicated that by defining a proper objective function and considering an appropriate penalty function,near global optimum values of regressors can be easily obtained.The results attest the high potentials of swarm and evolutionary techniques for fast,accurate and robust regularized machine learning.Originality/value–The originality of the research paper lies behind the use of a novel constraint metaheuristic computing scheme which can be used for effective regularized optimally pruned extreme learning machine(OP-ELM).The self-adaption of the proposed method alleviates the user from the knowledge of the underlying system,and also increases the degree of the automation of OP-ELM.Besides,by using different types of metaheuristics,it is demonstrated that the proposed methodology is a general flexible scheme,and can be combined with different types of swarm and evolutionary-based optimization techniques to form a regularized machine learning approach. 展开更多
关键词 Evolutionary computation Function approximation Hybrid systems
A Review of Genetic Algorithms:Principles, Procedures, and Applications in Optimization 认领 引用
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作者 M.A.El-Shorbagy 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第4期138-184,共47页
This paper provides a thorough examination of Genetic Algorithms(GAs),a category of evolutionary computation methods derived from the concepts of natural selection and genetics.The main concept and operational princip... This paper provides a thorough examination of Genetic Algorithms(GAs),a category of evolutionary computation methods derived from the concepts of natural selection and genetics.The main concept and operational principle of GAs are elucidated,highlighting the evolution of populations of candidate solutions across multiple generations to get optimal or near-optimal solutions for complicated problems.The paper delineates the sequential phases of a conventional GA,encompassing problem formulation,solution encoding,initialization of population,fitness evaluation,selection,crossover,mutation,and termination criteria,so offering a coherent framework for comprehending the algorithm’s functionality.Moreover,numerous prominent genetic operators,including crossover and mutation,are examined,highlighting their distinct forms and processes for fostering diversity and exploration within the search space.Also,the paper emphasizes the benefits of GAs,including their capacity to address nonlinear,multimodal,and high-dimensional optimization challenges without necessitating gradient information,along with their adaptability in resolving both continuous and discrete issues.The limitations and constraints of GAs,such as computing expense,parameter optimization,and the risk of premature convergence,are thoroughly analyzed.The paper examines various applications of GAs across fields,including engineering design,control systems,combinatorial optimization,machine learning,operations research,and multi-objective optimization,demonstrating the versatility and practical significance of this evolutionary method.This work establishes a robust basis for scholars and practitioners seeking to implement GAs in intricate optimization challenges.The review indicates that GAs have greatly progressed from Holland’s original formulation to specialized variations,such as real-valued,permutation,and tree-based encodings,each tailored to certain issue categories.The critical study indicates that although classical GAs are proficient in global exploration,their hybridization with local search techniques(memetic algorithms),swarm intelligence(GA-PSO),and surrogate models significantly improves convergence time and solution accuracy.The study highlights ongoing research deficiencies,such as the disparity between theoretical convergence proofs and the actual performance of algorithms,as well as the necessity for systematic recommendations in the design of hybrid algorithms. 展开更多
关键词 Genetic algorithm evolutionary computation global optimization nonlinear optimization computational intelligence optimization techniques
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Multi-Agent Swarm Optimization Method With Contribution-Based Cooperation for Distributed Multi-Target Localization and Data Association 认领 引用
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作者 Taiyou Chen Xiaomin Hu +1 位作者 Qiuzhen Lin Weineng Chen 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第7期1673-1688,共16页
With the development of communication and computation capabilities on terminal hardware,it is promising to apply distributed optimization methods to wireless sensor networks to improve the autonomous collaboration abi... With the development of communication and computation capabilities on terminal hardware,it is promising to apply distributed optimization methods to wireless sensor networks to improve the autonomous collaboration ability of sensors.In this work,we study distributed multi-target localization problem with measurement-to-measurement association(DM2M),where each sensor only accesses its own measurement data without the association of measurements from other sensors.We first reformulate DM2M into a distributed bilevel optimization problem to reduce the search space of negotiated variables caused by the data association among sensors.Then,we propose a multiagent swarm optimization method with contribution-based cooperation(MASTER).In MASTER,each sensor maintains a particle swarm to represent candidate solutions of target positions.Sensors evolve their particle swarms through two phases of local optimization and neighbor cooperation to locate the target cooperatively.To address the bilevel local objective function,we combine the Kuhn-Munkres algorithm and the competitive swarm optimization for local optimization.To promote sensors to optimize the global objective,we design a contribution-based cooperation method to guide sensors to learn from their neighbors.Through localization experiments for different target numbers and localization dimensions,the proposed algorithm achieves smaller localization errors and more stable consensus than existing algorithms. 展开更多
关键词 Distributed optimization evolutionary computation measurement-to-measurement association particle swarm optimization(PSO) wireless sensor networks(WSNs)
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Integrating Conjugate Gradients Into Evolutionary Algorithms for Large-Scale Continuous Multi-Objective Optimization 认领 引用 被引量：10
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作者 Ye Tian Haowen Chen +3 位作者 Haiping Ma Xingyi Zhang Kay Chen Tan Yaochu Jin 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2022年第10期1801-1817,共17页
Large-scale multi-objective optimization problems(LSMOPs)pose challenges to existing optimizers since a set of well-converged and diverse solutions should be found in huge search spaces.While evolutionary algorithms a... Large-scale multi-objective optimization problems(LSMOPs)pose challenges to existing optimizers since a set of well-converged and diverse solutions should be found in huge search spaces.While evolutionary algorithms are good at solving small-scale multi-objective optimization problems,they are criticized for low efficiency in converging to the optimums of LSMOPs.By contrast,mathematical programming methods offer fast convergence speed on large-scale single-objective optimization problems,but they have difficulties in finding diverse solutions for LSMOPs.Currently,how to integrate evolutionary algorithms with mathematical programming methods to solve LSMOPs remains unexplored.In this paper,a hybrid algorithm is tailored for LSMOPs by coupling differential evolution and a conjugate gradient method.On the one hand,conjugate gradients and differential evolution are used to update different decision variables of a set of solutions,where the former drives the solutions to quickly converge towards the Pareto front and the latter promotes the diversity of the solutions to cover the whole Pareto front.On the other hand,objective decomposition strategy of evolutionary multi-objective optimization is used to differentiate the conjugate gradients of solutions,and the line search strategy of mathematical programming is used to ensure the higher quality of each offspring than its parent.In comparison with state-of-the-art evolutionary algorithms,mathematical programming methods,and hybrid algorithms,the proposed algorithm exhibits better convergence and diversity performance on a variety of benchmark and real-world LSMOPs. 展开更多
关键词 Conjugate gradient differential evolution evolutionary computation large-scale multi-objective optimization mathematical programming
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