A Survey on Evolutionary Constrained Multiobjective Optimization

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...

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Vydáno v:IEEE transactions on evolutionary computation Ročník 27; číslo 2; s. 201 - 221
Hlavní autoři: Liang, Jing, Ban, Xuanxuan, Yu, Kunjie, Qu, Boyang, Qiao, Kangjia, Yue, Caitong, Chen, Ke, Tan, Kay Chen
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.04.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Benchmark test problems
Benchmark testing
constrained multiobjective optimization
constraint handling
Constraints
Convergence
Evolutionary algorithms
Evolutionary computation
Literature reviews
Multiple objective analysis
Optimization
Pareto optimization
Sociology
Statistics
ISSN:1089-778X, 1941-0026
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Shrnutí: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.
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ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2022.3155533