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Evolutionary Computation for large-scale multi-objective optimization: A Decade of Progresses

International Journal of Automation and computing 2021年第2期18卷 155-169页

作者： Wen-Jing Hong Peng Yang Ke Tang Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and EngineeringSouthern University of Science and TechnologyShenzhen 518055China Department of Management Science University of Science and Technology of ChinaHefei 230027China Guangdong–Hong Kong–Macao Greater Bay Area Center for Brain Science and Brain–inspired Intelligence Guangzhou 510515China

large-scale multi-objective optimization problems(MOPs)that involve a large number of decision variables,have emerged from many real-world *** 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 *** recently,it has been reported that traditional multi-objective EAs(MOEAs)suffer severe deterioration with the increase of decision *** 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 *** paper reviews the progress of evolutionary computation for large-scale multi-objective optimization from two *** 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 *** 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 *** future research directions are also discussed.

关键词： large-scale multi-objective optimization high-dimensional search space evolutionary computation evolutionary algorithms scalability

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维普期刊数据库

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Integrating Conjugate Gradients Into Evolutionary Algorithms for large-scale Continuous multi-objective optimization

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IEEE/CAA Journal of Automatica Sinica 2022年第10期9卷 1801-1817页

作者： Ye Tian Haowen Chen Haiping Ma Xingyi Zhang Kay Chen Tan Yaochu Jin the Information Materials and Intelligent Sensing Laboratory of Anhui Province Institutes of Physical Science and Information TechnologyAnhui UniversityHefei 230601China the Institute of Artificial Intelligence Hefei Comprehensive National Science CenterHefei 230601China IEEE the Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial IntelligenceAnhui UniversityHefei 230601China the Department of Computing The Hong Kong Polytechnic UniversityHong Kong SARChina the Faculty of Technology Bielefeld UniversityBielefeld 33619Germany

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 *** evolutionary algorithms are good at solving small-scale multi-objective optimization problems,they are criticized for low efficiency in converging to the optimums of *** contrast,mathematical programming methods offer fast convergence speed on large-scale single-objective optimization problems,but they have difficulties in finding diverse solutions for ***,how to integrate evolutionary algorithms with mathematical programming methods to solve LSMOPs remains *** this paper,a hybrid algorithm is tailored for LSMOPs by coupling differential evolution and a conjugate gradient *** 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 *** 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 *** 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.

关键词： Conjugate gradient differential evolution evolutionary computation large-scale multi-objective optimization mathematical programming

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维普期刊数据库

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A Matrix Adaptation Evolution Strategy Based Evolution Algorithm for large-scale Many-objective optimization

A Matrix Adaptation Evolution Strategy Based Evolution Algor...

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第32届中国控制与决策会议

作者： Ling Yang Jianchang Liu Fei Li Shubin Tan Tianzi Zheng Yuanchao Liu School of Information Science and Engineering Northeastern University Department of Electrical and Information Engineering Anhui University of Technology

In recent years,multi-objective optimization problems have been received a lot of ***,many applications involve hundreds or thousands of decision variables,which pose a serious barrier for evolutionary *** this paper,therefore,a novel algorithm,denoted as matrix adaptation evolution strategy based evolution algorithm(MAES-EA),is proposed for large-scale multi-objective *** the proposed algorithm,the decision variables are divided into convergence related variables and diversity related variables,which are optimized by different optimization *** be specific,the matrix adaptation evolution strategy is introduced to reduce the amount of evaluations when optimizing the convergence related *** addition,the L-norm-based distance is applied to optimize diversity related *** performance of the proposed algorithm is compared with some state-ofthe-art methods on LSMOP test instances,and results show that MAES-EA is suitable for large-scale multi-objective optimization.

关键词： MAES large-scale multi-objective optimization decision variables evolution algorithm

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