Multi-objective multitasking optimization based on positive knowledge transfer mechanism

Abstract

Multi-objective multitasking optimization (MTO) is an emerging research topic in the field of evolutionary computation, which can solve multiple optimization tasks simultaneously and improve the convergence speed of each task. Recently, it has been demonstrated that the useful knowledge is always hidden in valuable solutions. When solving MTO problems, the core issue is how to select valuable solutions from the source task to help the target task. In this study, a multi-objective evolutionary multitasking algorithm based on positive knowledge transfer mechanism is proposed. Specifically, a cheap surrogate model is introduced to evaluate the quality of the solutions, which can find valuable solutions. Moreover, a diversity maintenance method is designed to maintain the diversity of solutions in each task. Finally, the selection strategy of transferred solutions is put forward to find valuable solutions with good diversity, which can improve the efficiency of positive knowledge transfer. Experiments on two MTO test suites and a real-world case demonstrate that the proposed algorithm is effective and competitive.