文献类型：期刊文献

英文题名：Cluster-Based Regression Transfer Learning for Dynamic Multi-Objective Optimization

作者：Zhang, Xi[1];Qian, Feng[1];Zhang, Liping[2]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Shanghai 200237, Peoples R China;[2]Shanxi Agr Univ, Inst Cotton Res, Yuncheng 044000, Peoples R China

年份：2023

卷号：11

期号：2

外文期刊名：PROCESSES

收录：;EI(收录号：20241515855772);WOS:【SCI-EXPANDED(收录号:WOS:000941709600001)】；

基金：This work was supported by the National Natural Science Foundation of China (No. 61988101, 62136003).

语种：英文

外文关键词：dynamic multi-objective optimization; evolutionary algorithm; regression transfer; transfer learning

摘要：Many multi-objective optimization problems in the real world have conflicting objectives, and these objectives change over time, known as dynamic multi-objective optimization problems (DMOPs). In recent years, transfer learning has attracted growing attention to solve DMOPs, since it is capable of leveraging historical information to guide the evolutionary search. However, there is still much room for improvement in the transfer effect and the computational efficiency. In this paper, we propose a cluster-based regression transfer learning-based dynamic multi-objective evolutionary algorithm named CRTL-DMOEA. It consists of two components, which are the cluster-based selection and cluster-based regression transfer. In particular, once a change occurs, we employ a cluster-based selection mechanism to partition the previous Pareto optimal solutions and find the clustering centroids, which are then fed into autoregression prediction model. Afterwards, to improve the prediction accuracy, we build a strong regression transfer model based on TrAdaboost.R2 by taking advantage of the clustering centroids. Finally, a high-quality initial population for the new environment is predicted with the regression transfer model. Through a comparison with some chosen state-of-the-art algorithms, the experimental results demonstrate that the proposed CRTL-DMOEA is capable of improving the performance of dynamic optimization on different test problems.