A Review of Evolutionary Multimodal Multiobjective Optimization

被引：129
|
作者：
Tanabe, Ryoji [1 ]
Ishibuchi, Hisao [1 ]
机构：
[1] Southern Univ Sci & Technol, Univ Key Lab Evolving Intelligent Syst Guangdong, Dept Comp Sci & Engn, Shenzhen Key Lab Computat Intelligence, Shenzhen 518055, Peoples R China
来源：
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION | 2020年 / 24卷 / 01期
基金：
中国国家自然科学基金;
关键词：
Evolutionary algorithms; multimodal multiobjective optimization; performance indicators; test problems; SELF-ADAPTATION; OMNI-OPTIMIZER; DECISION SPACE; NSGA-II; ALGORITHM; PERFORMANCE; DIVERSITY; EMOA; SELECTION; DISTANCE;
D O I：
10.1109/TEVC.2019.2909744
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Multimodal multiobjective optimization aims to find all Pareto optimal solutions, including overlapping solutions in the objective space. Multimodal multiobjective optimization has been investigated in the evolutionary computation community since 2005. However, it is difficult to survey existing studies in this field because they have been independently conducted and do not explicitly use the term "multimodal multiobjective optimization." To address this issue, this letter reviews the existing studies of evolutionary multimodal multiobjective optimization, including studies published under names that are different from multimodal multiobjective optimization. Our review also clarifies open issues in this research area.
引用
收藏
页码：193 / 200
页数：8
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