Multitasking multiobjective optimization based on transfer component analysis

Multitasking optimization (MTO) has emerged as a new research topic in recent years. The purpose of MTO is to use the correlations between tasks to find a set of optimal solutions to simultaneously optimize multiple tasks. MTO research focuses on promoting positive transfer of knowledge and sufficient information exchange between tasks. To positively promote the efficiency of knowledge transfer, a multiobjective multifactorial evolutionary algorithm based on transfer component analysis (TCA) and differential evolution (DE) called TCADE is proposed. The TCA method is used to construct a dimensionality reduction subspace, in which the correlation between two tasks is used to find a set of solutions. Co-evolution of multiple populations is promoted after explicit transfer of the solutions. Furthermore, a DE operator is used to generate more diverse individuals. TCADE effectively utilizes the potential relationships between tasks to transfer solutions across them and promotes knowledge transfer between them. TCADE is tested by experiments on nine benchmark problems. The experimental results show that the proposed algorithm obtains 15 inverted generational distance optimal values for 18 test functions. (C) 2022 Elsevier Inc. All rights reserved.