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• W4214710237 endingPage "101055" @default.
• W4214710237 startingPage "101055" @default.
• W4214710237 abstract "Balancing between the optimization of objective functions and constraint satisfaction is essential to handle constrained multi-objective optimization problems (CMOPs). Recently, various methods have been presented to enhance the performance for the constrained multi-objective optimization evolutionary algorithms (CMOEAs). However, most of them encounter difficulties when dealing with the CMOPs with complex feasible regions. To overcome this drawback, this paper proposes a tri-population based co-evolutionary framework (TriP): i) the first and second populations are evolved through a weak co-evolutionary relation for the original and unconstrained problems respectively to handle CMOPs with relatively simple constraints; and ii) the third population is evolved solely for the constraint relaxed problem with constraint relaxation technique. The cooperation of three populations preserve the advantages of weak co-evolution and constraint relaxation. Experiments on six benchmark CMOPs with 65 instances and diverse features are performed. Compared to 9 state-of-the-art CMOEAs, the proposed framework yields highly competitive performance and the best versatility. In addition, the effectiveness of the proposed framework on handling real-world CMOPs is also verified." @default.
• W4214710237 created "2022-03-02" @default.
• W4214710237 creator A5013105439 @default.
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• W4214710237 date "2022-04-01" @default.
• W4214710237 modified "2023-10-10" @default.
• W4214710237 title "A tri-population based co-evolutionary framework for constrained multi-objective optimization problems" @default.
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• W4214710237 doi "https://doi.org/10.1016/j.swevo.2022.101055" @default.
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