A Survey on Evolutionary Constrained Multiobjective Optimization

被引：181
作者：
Liang, Jing [1 ]
Ban, Xuanxuan [1 ]
Yu, Kunjie [1 ]
Qu, Boyang [2 ]
Qiao, Kangjia [1 ]
Yue, Caitong [1 ]
Chen, Ke [1 ]
Tan, Kay Chen [3 ]
机构：
[1] Zhengzhou Univ, Sch Elect Engn, Zhengzhou 450001, Peoples R China
[2] Zhongyuan Univ Technol, Sch Elect & Informat Engn, Zhengzhou 450007, Peoples R China
[3] Hong Kong Polytech Univ, Dept Comp, Hong Kong, Peoples R China
来源：
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION | 2023年 / 27卷 / 02期
基金：
中国国家自然科学基金; 中国博士后科学基金;
关键词：
Optimization; Convergence; Benchmark testing; Pareto optimization; Statistics; Sociology; Evolutionary computation; Benchmark test problems; constrained multiobjective optimization; constraint handling; evolutionary algorithms; PARTICLE SWARM OPTIMIZATION; VEHICLE-ROUTING PROBLEM; DIFFERENTIAL EVOLUTION; GENETIC ALGORITHM; INFEASIBLE SOLUTIONS; DESIGN OPTIMIZATION; SEARCH; SYSTEM; OBJECTIVES; OPERATORS;
D O I：
10.1109/TEVC.2022.3155533
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Handling constrained multiobjective optimization problems (CMOPs) is extremely challenging, since multiple conflicting objectives subject to various constraints require to be simultaneously optimized. To deal with CMOPs, numerous constrained multiobjective evolutionary algorithms (CMOEAs) have been proposed in recent years, and they have achieved promising performance. However, there has been few literature on the systematic review of the related studies currently. This article provides a comprehensive survey for evolutionary constrained multiobjective optimization. We first review a large number of CMOEAs through categorization and analyze their advantages and drawbacks in each category. Then, we summarize the benchmark test problems and investigate the performance of different constraint handling techniques (CHTs) and different algorithms, followed by some emerging and representative applications of CMOEAs. Finally, we discuss some new challenges and point out some directions of the future research in the field of evolutionary constrained multiobjective optimization.
引用
收藏
页码：201 / 221
页数：21
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