Multiple source transfer learning for dynamic multiobjective optimization

Yulong Ye, Qiuzhen Lin, Lijia Ma, Ka-Chun Wong, Maoguo Gong, Carlos A. Coello Coello

Abstract: Highlights•A hierarchical clustering-based manifold transfer learning (CMTL) method is developed for dynamic multi-objective optimization problems.•A multi-source transfer learning (MSTL) method is designed to fully exploit all historical knowledge, which can excavate valuable knowledge from the historical environment to predict a high-quality initial population for the new environment.•By incorporating the proposed CMTL and MSTL transfer learning methods, a new DMOEA, called MSTL-DMOEA, is proposed for dynamic optimization.•The superiority of the proposed MSTL-DMOEA over existing prediction-based and transfer-based methods is demonstrated.