A Self-Adaptive Evolutionary Multi-Task Based Constrained Multi-Objective Evolutionary Algorithm

Constrained multi-objective optimization problems (CMOPs) are difficult to solve since they involve the optimization of multiple objectives and the satisfaction of various constraints. Most constrained multi-objective evolutionary algorithms (CMOEAs) are prone to fall into the local optima due to th...

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Bibliographic Details
Published in:	IEEE transactions on emerging topics in computational intelligence Vol. 7; no. 4; pp. 1 - 15
Main Authors:	Qiao, Kangjia, Liang, Jing, Yu, Kunjie, Wang, Minghui, Qu, Boyang, Yue, Caitong, Guo, Yinan
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 01.08.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Constrained multi-objective optimization Constraints Evolutionary algorithms evolutionary multi-task optimization Genetic algorithms inter-task intra-task Knowledge transfer Multiple objective analysis Multitasking Optimization Populations self-adaptive Sociology Statistics Task analysis
ISSN:	2471-285X, 2471-285X
Online Access:	Get full text
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