Dynamic adaptive multi-objective optimization algorithm based on type detection

doi:10.1016/j.ins.2023.119867

CORC > 自动化研究所 > 中国科学院自动化研究所 > 精密感知与控制研究中心 > 人工智能与机器学习

	Dynamic adaptive multi-objective optimization algorithm based on type detection
	Cai, Xingjuan 1,2; Wu, Linjie 1; Zhao, Tianhao 1; Wu, Di 3; Zhang, Wensheng 4; Chen, Jinjun 1,5
刊名	INFORMATION SCIENCES
	2024
卷号	654 页码:16
关键词	Adaptive response strategy Type detection Dynamic multi-objective optimization Transfer learning
ISSN号	0020-0255
DOI	10.1016/j.ins.2023.119867
通讯作者	Wu, Linjie(wulinjie19971013@163.com)
英文摘要	Dynamic multi-objective optimization problems (DMOPs) are multiobjective problems that are influenced by dynamically changing environmental parameters. Most current algorithms for solving DMOPs only respond to dynamic changes in the decision space or objective space and also ignore the impact of the type of DMOPs on the algorithm. The changes in the Paretooptimal solution (POS) and Pareto-optimal front (POF) may affect the type of change in DMOPs. Therefore, this paper proposed an adaptive dynamic multi-objective evolutionary algorithm for type detection (TDA-DMOEA). First, the dynamic detection operator is designed to identify the types of dynamic problems. The Wilcoxon signed-rank test and Hyper Volume (HV) are used to detect the difference of POS and POF in two adjacent environments respectively. Then, different response strategies are designed to cope with different types of changes in DMOP. In particular, a multi-angle-based transfer learning method (MA-TL) with a closed kernel function is derived when faced with simultaneous changes in POS and POF. Finally, a comprehensive study of the commonly used benchmark set of DMOPs is presented, and the proposed algorithm achieves better performance in optimizing DMOPs.
资助项目	Science and Technology Development Foundation of the Central Guiding Local[YDZJSX2021A038] ; National Natural Science Foundation of China[61806138] ; Open Fund of State Key Laboratory for Novel Software Technology (NanjingUniversity)[KFKT2022B18]
WOS关键词	PREDICTION ; STRATEGY ; KNEE
WOS研究方向	Computer Science
语种	英语
出版者	ELSEVIER SCIENCE INC
WOS记录号	WOS:001114405000001
资助机构	Science and Technology Development Foundation of the Central Guiding Local ; National Natural Science Foundation of China ; Open Fund of State Key Laboratory for Novel Software Technology (NanjingUniversity)
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/55174]
专题	精密感知与控制研究中心_人工智能与机器学习
通讯作者	Wu, Linjie
作者单位	1.Taiyuan Univ Sci & Technol, Shanxi Key Lab Big Data Anal & Parallel Comp, Taiyuan 030024, Peoples R China 2.Nanjing Univ, State Key Lab Novel Software Technol, Nanjing, Peoples R China 3.Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China 4.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing, Peoples R China 5.Swinburne Univ Technol, Dept Comp Sci & Software Engn, Melbourne 3000, Australia
推荐引用方式 GB/T 7714	Cai, Xingjuan,Wu, Linjie,Zhao, Tianhao,et al. Dynamic adaptive multi-objective optimization algorithm based on type detection[J]. INFORMATION SCIENCES,2024,654:16.
APA	Cai, Xingjuan,Wu, Linjie,Zhao, Tianhao,Wu, Di,Zhang, Wensheng,&Chen, Jinjun.(2024).Dynamic adaptive multi-objective optimization algorithm based on type detection.INFORMATION SCIENCES,654,16.
MLA	Cai, Xingjuan,et al."Dynamic adaptive multi-objective optimization algorithm based on type detection".INFORMATION SCIENCES 654(2024):16.