Evolutionary Multitasking With Global and Local Auxiliary Tasks for Constrained Multi-Objective Optimization

doi:10.1109/JAS.2023.123336

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	Evolutionary Multitasking With Global and Local Auxiliary Tasks for Constrained Multi-Objective Optimization
	Kangjia Qiao; Jing Liang; Zhongyao Liu; Kunjie Yu; Caitong Yue; Boyang Qu
刊名	IEEE/CAA Journal of Automatica Sinica
	2023
卷号	10 期号:10 页码:1951-1964
关键词	Constrained multi-objective optimization evolutionary multitasking (EMT) global auxiliary task knowledge transfer local auxiliary task
ISSN号	2329-9266
DOI	10.1109/JAS.2023.123336
英文摘要	Constrained multi-objective optimization problems (CMOPs) include the optimization of objective functions and the satisfaction of constraint conditions, which challenge the solvers. To solve CMOPs, constrained multi-objective evolutionary algorithms (CMOEAs) have been developed. However, most of them tend to converge into local areas due to the loss of diversity. Evolutionary multitasking (EMT) is new model of solving complex optimization problems, through the knowledge transfer between the source task and other related tasks. Inspired by EMT, this paper develops a new EMT-based CMOEA to solve CMOPs, in which the main task, a global auxiliary task, and a local auxiliary task are created and optimized by one specific population respectively. The main task focuses on finding the feasible Pareto front (PF), and global and local auxiliary tasks are used to respectively enhance global and local diversity. Moreover, the global auxiliary task is used to implement the global search by ignoring constraints, so as to help the population of the main task pass through infeasible obstacles. The local auxiliary task is used to provide local diversity around the population of the main task, so as to exploit promising regions. Through the knowledge transfer among the three tasks, the search ability of the population of the main task will be significantly improved. Compared with other state-of-the-art CMOEAs, the experimental results on three benchmark test suites demonstrate the superior or competitive performance of the proposed CMOEA.
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/52395]
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,Zhongyao Liu,et al. Evolutionary Multitasking With Global and Local Auxiliary Tasks for Constrained Multi-Objective Optimization[J]. IEEE/CAA Journal of Automatica Sinica,2023,10(10):1951-1964.
APA	Kangjia Qiao,Jing Liang,Zhongyao Liu,Kunjie Yu,Caitong Yue,&Boyang Qu.(2023).Evolutionary Multitasking With Global and Local Auxiliary Tasks for Constrained Multi-Objective Optimization.IEEE/CAA Journal of Automatica Sinica,10(10),1951-1964.
MLA	Kangjia Qiao,et al."Evolutionary Multitasking With Global and Local Auxiliary Tasks for Constrained Multi-Objective Optimization".IEEE/CAA Journal of Automatica Sinica 10.10(2023):1951-1964.