Dynamic adaptive multi-objective optimization algorithm based on type detection

doi:10.1016/j.ins.2023.119867

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 人工智能与机器学习（杨雪冰）-技术团队

	Dynamic adaptive multi-objective optimization algorithm based on type detection
	Cai, Xingjuan 1,2; Wu, Linjie 1; Zhao, Tianhao 1; Wu, Di 3; Zhang, Wensheng4 ; Chen, Jinjun 1,5
发表期刊	INFORMATION SCIENCES
ISSN	0020-0255
	2024
卷号	654 页码:16
通讯作者	Wu, Linjie(wulinjie19971013@163.com)
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.
关键词	Adaptive response strategy Type detection Dynamic multi-objective optimization Transfer learning
DOI	10.1016/j.ins.2023.119867
关键词[WOS]	PREDICTION ; STRATEGY ; KNEE
收录类别	SCI
语种	英语
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]
项目资助者	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)
WOS研究方向	Computer Science
WOS类目	Computer Science, Information Systems
WOS记录号	WOS:001114405000001
出版者	ELSEVIER SCIENCE INC
引用统计	被引频次：3[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/55175
专题	多模态人工智能系统全国重点实验室_人工智能与机器学习（杨雪冰）-技术团队
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.