Indicator-Based Constrained Multiobjective Evolutionary Algorithms

Solving constrained multiobjective optimization problems (CMOPs) is a challenging task since it is necessary to optimize several conflicting objective functions and handle various constraints simultaneously. A promising way to solve CMOPs is to integrate multiobjective evolutionary algorithms (MOEAs...

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Vydáno v:IEEE transactions on systems, man, and cybernetics. Systems Ročník 51; číslo 9; s. 5414 - 5426
Hlavní autoři: Liu, Zhi-Zhong, Wang, Yong, Wang, Bing-Chuan
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.09.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Automation
Constrained multiobjective evolutionary algorithms (CMOEAs)
constrained multiobjective optimization problems (CMOPs)
constraint-handling technique
Constraints
Cybernetics
Decision feedback equalizers
Evolutionary algorithms
Evolutionary computation
Genetic algorithms
Handling
indicator
Linear programming
Multiple objective analysis
Optimization
Pareto optimization
ISSN:2168-2216, 2168-2232
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Shrnutí:Solving constrained multiobjective optimization problems (CMOPs) is a challenging task since it is necessary to optimize several conflicting objective functions and handle various constraints simultaneously. A promising way to solve CMOPs is to integrate multiobjective evolutionary algorithms (MOEAs) with constraint-handling techniques, and the resultant algorithms are called constrained MOEAs (CMOEAs). At present, many attempts have been made to combine dominance-based and decomposition-based MOEAs with diverse constraint-handling techniques together. However, for another main branch of MOEAs, i.e., indicator-based MOEAs, almost no effort has been devoted to extending them for solving CMOPs. In this article, we make the first study on the possibility and rationality of combining indicator-based MOEAs with constraint-handling techniques together. Afterward, we develop an indicator-based CMOEA framework which can combine indicator-based MOEAs with constraint-handling techniques conveniently. Based on the proposed framework, nine indicator-based CMOEAs are developed. Systemic experiments have been conducted on 19 widely used constrained multiobjective optimization test functions to identify the characteristics of these nine indicator-based CMOEAs. The experimental results suggest that both indicator-based MOEAs and constraint-handing techniques play very important roles in the performance of indicator-based CMOEAs. Some practical suggestions are also given about how to select appropriate indicator-based CMOEAs. Besides, we select a superior approach from these nine indicator-based CMOEAs and compare its performance with five state-of-the-art CMOEAs. The comparison results suggest that the selected indicator-based CMOEA can obtain quite competitive performance. It is thus believed that this article would encourage researchers to pay more attention to indicator-based CMOEAs in the future.
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ISSN:2168-2216
2168-2232
DOI:10.1109/TSMC.2019.2954491