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- W4313461204 abstract "Solving constrained multi-objective 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 paper proposes a new multitasking framework for CMOPs, borrowing the idea of evolutionary multitasking optimization. The main contributions are i) 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 multi-objective evolutionary algorithms; ii) a knowledge expression and a transfer strategy are devised to transfer the knowledge among the tasks; and iii) 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." @default.
- W4313461204 created "2023-01-06" @default.
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- W4313461204 date "2023-01-01" @default.
- W4313461204 modified "2023-10-03" @default.
- W4313461204 title "Constrained Multi-objective Optimization via Multitasking and Knowledge Transfer" @default.
- W4313461204 doi "https://doi.org/10.1109/tevc.2022.3230822" @default.
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