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- W4386002848 abstract "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 objective-centric 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." @default.
- W4386002848 created "2023-08-20" @default.
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- W4386002848 date "2023-11-01" @default.
- W4386002848 modified "2023-10-12" @default.
- W4386002848 title "Design and analysis of helper-problem-assisted evolutionary algorithm for constrained multiobjective optimization" @default.
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- W4386002848 doi "https://doi.org/10.1016/j.ins.2023.119547" @default.
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