Constrained Multiobjective Optimization via Multitasking and Knowledge Transfer

Solving constrained multiobjective optimization problems (CMOPs) with various features and challenges via evolutionary algorithms is very popular. Existing methods usually adopt an additional helper problem to simplify and solve them by divide and conquer. This article proposes a new multitasking fr...

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Veröffentlicht in:IEEE transactions on evolutionary computation Jg. 28; H. 1; S. 77 - 89
Hauptverfasser: Ming, Fei, Gong, Wenyin, Wang, Ling, Gao, Liang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 01.02.2024
Schlagworte:
Constraints
Evolutionary algorithms
Knowledge management
Multiple objective analysis
Multitasking
Optimization
ISSN:1089-778X, 1941-0026
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Zusammenfassung:Solving constrained multiobjective optimization problems (CMOPs) with various features and challenges via evolutionary algorithms is very popular. Existing methods usually adopt an additional helper problem to simplify and solve them by divide and conquer. This article proposes a new multitasking framework for CMOPs, borrowing the idea of evolutionary multitasking optimization. The main contributions are: 1) a multitasking framework is proposed, where a CMOP is modeled as a multitasking optimization problem with three tasks. Then, it is solved by constraint-first, constraint-ignored, and constraint-relaxed multiobjective evolutionary algorithms; 2) a knowledge expression and a transfer strategy are devised to transfer the knowledge among the tasks; and 3) based on the proposed framework, a new two-stage algorithm is presented to solve CMOPs. The effectiveness of our approach is validated through experiments on four CMOP benchmark suites and 19 real-world CMOPs.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2022.3230822