期刊
INFORMATION SCIENCES
卷 560, 期 -, 页码 68-91出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2021.01.029
关键词
Evolutionary algorithm; Constraint-handling priority; Constrained multi-objective optimization
资金
- National Key Research and Development Project [2018AAA0100105]
- National Natural Science Foundation of China [61822301, 61672033, 61906001, U1804262]
- Hong Kong Scholars Program [XJ2019035]
- Anhui Provincial Natural Science Foundation [1808085J06, 1908085QF271]
- CCF-Tencent Open Research Fund [CCF-Tencent RAGR20200121]
Constrained multi-objective optimization problems are challenging to handle due to the complexities of objectives and constraints. To address this issue, a multi-stage evolutionary algorithm is proposed in this paper, which gradually adds constraints and sorts their handling priority based on their impact on the Pareto front. Experimental results demonstrate that the proposed algorithm outperforms state-of-the-art algorithms in dealing with complex constraint problems.
Constrained multi-objective optimization problems (CMOPs) are difficult to handle because objectives and constraints need to be considered simultaneously, especially when the constraints are extremely complex. Some recent algorithms work well when dealing with CMOPs with a simple feasible region; however, the effectiveness of most algorithms degrades considerably for CMOPs with complex feasible regions. To address this issue, this paper proposes a multi-stage evolutionary algorithm, where constraints are added one after the other and handled in different stages of evolution. Specifically, in the early stages, the algorithm only considers a small number of constraints, which can make the population efficiently converge to the potential feasible region with good diversity. As the algorithm moves to the later stages, more constraints are considered to search the optimal solutions based on the solutions obtained in the previous stages. Furthermore, a strategy for sorting the constraint-handling priority according to the impact on the unconstrained Pareto front is proposed, which can accelerate the convergence of the algorithm. Experimental results on five benchmark suites and three real-world applications showed that the proposed algorithm outperforms several state-of-the-art constraint multi-objective evolutionary algorithms when dealing with CMOPs, especially for problems with complex constraints. (C) 2021 Elsevier Inc. All rights reserved.
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