Constraints Separation Based Evolutionary Multitasking for Constrained Multi-Objective Optimization Problems

doi:10.1109/JAS.2024.124545

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	Constraints Separation Based Evolutionary Multitasking for Constrained Multi-Objective Optimization Problems
	Kangjia Qiao; Jing Liang; Kunjie Yu; Xuanxuan Ban; Caitong Yue; Boyang Qu; Ponnuthurai Nagaratnam Suganthan
刊名	IEEE/CAA Journal of Automatica Sinica
	2024
卷号	11 期号:8 页码:1819-1835
关键词	Constrained multi-objective optimization (CMOPs) evolutionary multitasking knowledge transfer single constraint
ISSN号	2329-9266
DOI	10.1109/JAS.2024.124545
英文摘要	Constrained multi-objective optimization problems (CMOPs) generally contain multiple constraints, which not only form multiple discrete feasible regions but also reduce the size of optimal feasible regions, thus they propose serious challenges for solvers. Among all constraints, some constraints are highly correlated with optimal feasible regions; thus they can provide effective help to find feasible Pareto front. However, most of the existing constrained multi-objective evolutionary algorithms tackle constraints by regarding all constraints as a whole or directly ignoring all constraints, and do not consider judging the relations among constraints and do not utilize the information from promising single constraints. Therefore, this paper attempts to identify promising single constraints and utilize them to help solve CMOPs. To be specific, a CMOP is transformed into a multitasking optimization problem, where multiple auxiliary tasks are created to search for the Pareto fronts that only consider a single constraint respectively. Besides, an auxiliary task priority method is designed to identify and retain some high-related auxiliary tasks according to the information of relative positions and dominance relationships. Moreover, an improved tentative method is designed to find and transfer useful knowledge among tasks. Experimental results on three benchmark test suites and 11 real-world problems with different numbers of constraints show better or competitive performance of the proposed method when compared with eight state-of-the-art peer methods.
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/58550]
专题	自动化研究所_学术期刊_IEEE/CAA Journal of Automatica Sinica
推荐引用方式 GB/T 7714	Kangjia Qiao,Jing Liang,Kunjie Yu,et al. Constraints Separation Based Evolutionary Multitasking for Constrained Multi-Objective Optimization Problems[J]. IEEE/CAA Journal of Automatica Sinica,2024,11(8):1819-1835.
APA	Kangjia Qiao.,Jing Liang.,Kunjie Yu.,Xuanxuan Ban.,Caitong Yue.,...&Ponnuthurai Nagaratnam Suganthan.(2024).Constraints Separation Based Evolutionary Multitasking for Constrained Multi-Objective Optimization Problems.IEEE/CAA Journal of Automatica Sinica,11(8),1819-1835.
MLA	Kangjia Qiao,et al."Constraints Separation Based Evolutionary Multitasking for Constrained Multi-Objective Optimization Problems".IEEE/CAA Journal of Automatica Sinica 11.8(2024):1819-1835.