History-Assisted Two-State Auxiliary Task Collaboration Approach for Dynamic Constrained Multiobjective Optimization

Dynamic constrained multiobjective optimization problems (DCMOPs) are widely encountered in real-world applications and have attracted increasing attention in the evolutionary computation community. Existing studies primarily focus on the population initialization, but disregard to improve the evolu...

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Vydané v:	IEEE transactions on evolutionary computation s. 1
Hlavní autori:	Zhang, Dezheng, Yu, Kunjie, Liang, Jing, Qiao, Kangjia, Qu, Boyang, Chen, Ke, Yue, Caitong, Wang, Ling
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	IEEE 2024
Predmet:	Dynamic constrained multiobjective optimization Environment identify Evolutionary algorithm Evolutionary computation Heuristic algorithms Historical information Optimization Prediction algorithms Sociology Statistics Task analysis Two-state algorithm
ISSN:	1089-778X, 1941-0026
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