Enhancing Evolutionary Algorithms With Pattern Mining for Sparse Large-Scale Multi-Objective Optimization Problems

doi:10.1109/JAS.2024.124548

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	Enhancing Evolutionary Algorithms With Pattern Mining for Sparse Large-Scale Multi-Objective Optimization Problems
	Sheng Qi; Rui Wang; Tao Zhang; Weixiong Huang; Fan Yu; Ling Wang
刊名	IEEE/CAA Journal of Automatica Sinica
	2024
卷号	11 期号:8 页码:1786-1801
关键词	Evolutionary algorithms pattern mining sparse large-scale multi-objective problems (SLMOPs) sparse large-scale optimization
ISSN号	2329-9266
DOI	10.1109/JAS.2024.124548
英文摘要	Sparse large-scale multi-objective optimization problems (SLMOPs) are common in science and engineering. However, the large-scale problem represents the high dimensionality of the decision space, requiring algorithms to traverse vast expanse with limited computational resources. Furthermore, in the context of sparse, most variables in Pareto optimal solutions are zero, making it difficult for algorithms to identify non-zero variables efficiently. This paper is dedicated to addressing the challenges posed by SLMOPs. To start, we introduce innovative objective functions customized to mine maximum and minimum candidate sets. This substantial enhancement dramatically improves the efficacy of frequent pattern mining. In this way, selecting candidate sets is no longer based on the quantity of non-zero variables they contain but on a higher proportion of non-zero variables within specific dimensions. Additionally, we unveil a novel approach to association rule mining, which delves into the intricate relationships between non-zero variables. This novel methodology aids in identifying sparse distributions that can potentially expedite reductions in the objective function value. We extensively tested our algorithm across eight benchmark problems and four real-world SLMOPs. The results demonstrate that our approach achieves competitive solutions across various challenges.
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/58548]
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	Sheng Qi,Rui Wang,Tao Zhang,et al. Enhancing Evolutionary Algorithms With Pattern Mining for Sparse Large-Scale Multi-Objective Optimization Problems[J]. IEEE/CAA Journal of Automatica Sinica,2024,11(8):1786-1801.
APA	Sheng Qi,Rui Wang,Tao Zhang,Weixiong Huang,Fan Yu,&Ling Wang.(2024).Enhancing Evolutionary Algorithms With Pattern Mining for Sparse Large-Scale Multi-Objective Optimization Problems.IEEE/CAA Journal of Automatica Sinica,11(8),1786-1801.
MLA	Sheng Qi,et al."Enhancing Evolutionary Algorithms With Pattern Mining for Sparse Large-Scale Multi-Objective Optimization Problems".IEEE/CAA Journal of Automatica Sinica 11.8(2024):1786-1801.