Evolutionary Computation for Large-scale Multi-objective Optimization: A Decade of Progresses

作     者：Wen-Jing Hong Peng Yang Ke Tang Wen-Jing Hong;Peng Yang;Ke Tang

作者机构：Guangdong Provincial Key Laboratory of Brain-inspired Intelligent ComputationDepartment of Computer Science and EngineeringSouthern University of Science and TechnologyShenzhen 518055China Department of Management ScienceUniversity of Science and Technology of ChinaHefei 230027China Guangdong–Hong Kong–Macao Greater Bay Area Center for Brain Science and Brain–inspired IntelligenceGuangzhou 510515China 

出 版 物：《International Journal of Automation and computing》 (国际自动化与计算杂志（英文版）)

年 卷 期：2021年第18卷第2期

页      面：155-169页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This work was supported by the Natural Science Foundation of China(Nos.61672478 and 61806090) the National Key Research and Development Program of China(No.2017YFB1003102) the Guangdong Provincial Key Laboratory(No.2020B121201001) the Shenzhen Peacock Plan(No.KQTD2016112514355531) the Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-inspired Intelligence Fund(No.2019028) the Fellowship of China Postdoctoral Science Foundation(No.2020M671900) the National Leading Youth Talent Support Program of China 

主　　题：Large-scale multi-objective optimization high-dimensional search space evolutionary computation evolutionary algorithms scalability 

摘      要：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.