期刊
INFORMATION SCIENCES
卷 648, 期 -, 页码 -出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2023.119547
关键词
Evolutionary algorithm; Constrained multiobjective optimization; Inequality constraint; Equality constraint
In recent years, solving constrained multiobjective optimization problems by introducing simple helper problems has become popular. This study provides a comprehensive overview of existing constrained multiobjective evolutionary algorithms and proposes a novel helper-problem-assisted CMOEA, which has shown competitive performance in experiments.
In recent years, solving constrained multiobjective optimization problems (CMOPs) by introducing simple helper problems has become a popular concept. To date, no systematic study has investigated the conditions under which this concept operates. In this study, we presented a holistic overview of existing constrained multiobjective evolutionary algorithms (CMOEAs) to address three research questions: (1) Why do we introduce helper problems? (2) Which problems should be selected as helper problems? and (3) How do helper problems help? Based on these discussions, we developed a novel helper-problem-assisted CMOEA, where the original CMOP was solved by addressing a series of constraint-centric problems derived from the original problem, with their constraint boundaries shrinking gradually. At each stage, we also had an objectivecentric problem that was used to help solve the constraint-centric problem. In the experiments, we investigated the performance of the proposed algorithm on 66 benchmark problems and 15 real-world applications. The experimental results showed that the proposed algorithm is highly competitive compared with eight state-of-the-art CMOEAs.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据