文献类型：期刊文献

英文题名：Elitism-based transfer learning and diversity maintenance for dynamic multi-objective optimization

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

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Shanghai 200237, Peoples R China;[2]Nanjing Tech Univ, Inst Intelligent Mfg, Nanjing 211816, Peoples R China;[3]Bielefeld Univ, Fac Technol, Chair Nat Inspired Comp & Engn, D-33619 Bielefeld, Germany;[4]Univ Surrey, Dept Comp Sci, Guildford GU2 7XH, Surrey, England

年份：2023

卷号：636

外文期刊名：INFORMATION SCIENCES

收录：;EI(收录号：20231513865627);WOS:【SCI-EXPANDED(收录号:WOS:000980439000001)】；

基金：This work was supported by National Key Research & Development Program-Intergovernmental International Science and Technology Innovation Cooperation Project (2021YFE0112800) , National Natural Science Foundation of China (Key Program: 62136003) , National Natural Science Foundation of China (62103150) , China Postdoctoral Science Foundation (2021M691012) and the Young Elite Scientists Sponsorship Program by CAST under Grant 2022QNRC001.

语种：英文

外文关键词：Dynamic multi-objective optimization; Transfer learning; Evolutionary algorithm; Diversity maintenance

摘要：In handling dynamic multi-objective optimization problems (DMOPs), transfer learning driven methods have received considerable attention for finding a high-quality initial population with good convergence and diversity performance to adapt to the new environment. However, they commonly suffer from loss of population diversity and high computational consumption. Therefore, this study proposes a hybrid method combining elitism-based transfer learning and diversity maintenance to efficiently identify a high-quality initial population in response to environmental changes. An elite selection mechanism is developed to select elite individuals from the memory pool when the environment changes. Subsequently, an elitism-based transfer learning method is proposed to predict individuals by leveraging the knowledge from the selected elite individuals, thereby improving the computational efficiency and quality of the solutions. Subsequently, a random diversity maintenance strategy is developed to generate diverse individuals within the regions where the predicted individuals are located to defy the loss of diversity in the population. Finally, the generated diverse and predicted individuals are merged to form an initial population to adapt to the new environment. The experimental results have demonstrated the competitiveness of the proposed algorithm for most DMOP test instances in terms of convergence, diversity, and computational efficiency.