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Multi-task optimization (MTO) is related to the problem of simultaneous optimization of multiple optimization problems, for the purpose of solving these problems better in terms of optimization accuracy or time cost. To handle MTO problems, there emerges many evolutionary MTO (EMTO) algorithms, which possess distinguished strategies or frameworks in the aspect of handling the knowledge transfer between different optimization problems (tasks). In this paper, we explore the possibility of developing a more efficient EMTO solver based on differential evolution by introducing the strategies of a self-adaptive multi-task particle swarm optimization (SaMTPSO) algorithm, and by developing a new knowledge incorporation strategy. Then, we try to apply the proposed algorithm to solve the weapon–target assignment problem, which has never been explored in the field of EMTO before. Experiments were conducted on a popular MTO test benchmark and a WTA-MTO test set. Experimental results show that knowledge transfer in the proposed algorithm is effective and efficient, and EMTO is promising in solving WTA problems.

Self-Adaptive Multi-Task Differential Evolution Optimization: With Case Studies in Weapon–Target Assignment Problem