进化高维多目标优化研究综述  

Evolutionary Many-objective Optimization: A Survey

作　　者：徐康宇 刘元[1] 李密青 杨圣祥 邹娟[1] 郑金华[1] XU Kangyu;LIU Yuan;LI Miqing;YANG Shengxiang;ZOU Juan;ZHENG Jinhua(School of Computer Science&School of Cyberspace Security,Xiangtan University,Xiangtan 411100,China;School of Computer Science,Birmingham University,Birmingham B152TT,UK;School of Computer Science and Informatics,De Montfort University,Leicester LE19BH,UK)

机构地区：[1]湘潭大学计算机学院·网络空间安全学院,湖南湘潭411100 [2]伯明翰大学计算机科学学院,英国伯明翰B152TT [3]德蒙特福特大学计算机科学与信息学院,英国莱斯特LE19BH

出　　处：《控制工程》2023年第8期1436-1449,共14页Control Engineering of China

基　　金：国家自然科学基金资助项目(62176228,62276224);湖南省自然科学基金资助项目(2022JJ40452);湖南省教育厅一般项目(21C0077)。

摘　　要：高维多目标优化问题(many-objective optimization problems, MaOPs)已经普遍存在于工业和科学领域中,这类问题的目标数一般超过3个且目标之间存在冲突性。进化算法作为一种基于种群的元启发式搜索方法已经被证实能够有效求解MaOPs。近二十年来,高维多目标进化算法(many-objective evolutionary algorithms, MaOEAs)研究已取得了长足发展。现对进化高维多目标优化(evolutionary many-objective optimization, EMaO)的研究进展进行全面的综述,具体包括:(1)描述了EMaO的相关理论背景;(2)分析了EMaO面临的挑战;(3)详细讨论了Ma OEAs的发展概况;(4)归纳了Ma OPs以及性能指标;(5)介绍了面对高维目标空间的可视化工具;(6)总结了Ma OEAs在一些领域的应用;(7)剖析了进化算法在解决MaOPs时所面临的问题和挑战,并给出未来研究方向的建议。Many-objective optimization problems(MaOPs)widely exist in industrial and scientific fields,where there are more than 3 objectives that are conflicting with each other.As a class of population-based optimization algorithms inspired by biological evolution principles evolutionary algorithms have been proved to be effective in solving MaOPs.In the past two decades,the research on many-objective evolutionary algorithms(MaOEAs)has made great progress.This paper gives a comprehensive review on the research progress of evolutionary many-objective optimization(EMaO).Specifically,it includes:①Describing the relevant theoretical background of EMaO;②Analyzing the problems and challenges faced by evolutionary algorithms in solving MaOPs;③Discussing the development of MaOEAs in detaill;Summarizing MaOPs and performance indicators in detail;Introducing the visualization tools for high-dimensional objective space;?Summarizing the application of MaOEAs in some fields;Providing suggestions for future research in the domain.

关 键 词：进化高维多目标优化 进化算法 高维多目标优化问题 PARETO支配 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]