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A Survey on Evolutionary Constrained Multiobjective Optimization
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DOI：10.1109/TEVC.2022.3155533.png)
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Abstract
En 中文
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.
Keywords:
Optimization
Convergence
Benchmark testing
Pareto optimization
Statistics
Sociology
Evolutionary computation
Benchmark test problems
constrained multiobjective optimization
constraint handling
evolutionary algorithms
Journal
IF:
12
Papers:
1.8K
Citations:
2.4W
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