文献类型：期刊文献

英文题名：A self-adaptive dynamic multi-objective optimization algorithm based on transfer learning and elitism-based mutation

作者：Zhang, Xi[1];Jin, Yaochu[1,2,3];Qian, Feng[1,4]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Shanghai 200237, Peoples R China;[2]Bielefeld Univ, Fac Technol, Chair Nat Inspired Comp & Engn, D-33619 Bielefeld, Germany;[3]Univ Surrey, Dept Comp Sci, Guildford GU2 7XH, Surrey, England;[4]East China Univ Sci & Technol, Engn Res Ctr Proc Syst Engn, Minist Educ, Shanghai 200237, Peoples R China

年份：2023

卷号：559

外文期刊名：NEUROCOMPUTING

收录：;EI(收录号：20233914792434);WOS:【SCI-EXPANDED(收录号:WOS:001145573100001)】；

基金：The work was supported by National Natural Science Foundation of China (Key Program: 62136003) , National Natural Science Foundation of China (62293504, 62173144) , the Programme of Introducing Talents of Discipline to Universities (the 111 Project) under Grant B17017 and Fundamental Research Funds for the Central Universities.

语种：英文

外文关键词：Dynamic multi-objective optimization; Self-adaptive change response; Transfer learning; Diversity introduction

摘要：Dynamic multi-objective optimization problems (DMOPs) involve several conflicting objectives, and these objective functions change over time. Therefore, addressing DMOPs necessitates an effective response to environmental changes. However, most existing algorithms only deal with DMOPs with one particular type of environmental changes, whereas real-world dynamic changes are more complicated. Therefore, this paper proposes a self-adaptive DMOEA based on transfer learning and elitism-based mutation (ATM-DMOEA), aiming to efficiently tackle DMOPs exhibiting complex environmental changes. Specifically, a change evaluation method is devised to gauge change intensity and discern whether a change is drastic or gentle. Subsequently, an adaptive change response strategy is implemented to accommodate varying environmental changes. For drastic changes, the algorithm employs an elitism-based manifold transfer learning method, while gentle changes are handled with a diversity enhancement strategy introduced by adaptive elitism-based mutations with a varying mutation probability. The experiments have validated the competitiveness of the proposed ATM-DMOEA on the majority of DMOP test instances with different levels of change severity.