Multiobjective Multitask Optimization via Diversity-and Convergence-Oriented Knowledge Transfer

Multiobjective multitask optimization (MO-MTO) aims to exploit the similarities among different multiobjective optimization tasks through knowledge transfer (KT), facilitating their simultaneous resolution. The effective design of KT techniques embedded in multiobjective evolutionary optimizers is crucial for enhancing the performance of multiobjective multitask evolutionary algorithms (MO-MTEAs). However, a significant limitation of existing KT techniques in MO-MTEAs is their equal treatment of particles/individuals for transferred knowledge reception, which can negatively impact the balance of diversity and convergence in population evolution. To remedy this limitation, this article proposes a new MO-MTEA, named MTEA-DCK, which incorporates diversity-oriented KT (DKT) and convergence-oriented KT (CKT) techniques tailored for different particles in the population. MTEA-DCK utilizes a strength-Pareto-based competitive mechanism to divide particles into winners and losers: 1) for winners, DKT is conducted via an intertask domain alignment approach to enhance population diversity and 2) for losers, CKT is executed within the unified search space to improve convergence. Additionally, to ensure robust performance on complex task combinations, we introduce two automatic parameter control strategies specifically designed for these KT techniques. MTEA-DCK was performed on 39 benchmark MO-MTO problems and demonstrated superior performance compared to eight state-of-the-art MO-MTEAs and six multiobjective evolutionary algorithms. Finally, we present three real-world MO-MTO application cases, where our approach also yielded better results than other algorithms.