Differential Evolutionary Multi-Task Optimization

Evolutionary multi-task optimization (EMTO) studies on how to simultaneously solve multiple optimization problems, so-called component problems, via evolutionary algorithms, which has drawn much attention in the field of evolutionary computation. Knowledge transfer across multiple optimization problems (being solved) is the key to make EMTO to outperform traditional optimization paradigms. In this work, we propose a simple and effective knowledge transfer strategy which utilizes the best solution found so far for one problem to assist in solving the other problems during the optimization process. This strategy is based on random replacement. It does not introduce extra computational cost in terms of objective function evaluations for solving each component problem. However, it helps to improve optimization effectiveness and efficiency, compared to solving each component problem in a standalone way. This light-weight knowledge transfer strategy is implemented via differential evolution within a multi-population based EMTO paradigm, leading to a differential evolutionary multi-task optimization (DEMTO) algorithm. Experiments are conducted on the CEC'2017 competition test bed to compare the proposed DEMTO algorithm with five state-of-the-art EMTO algorithms, which demonstrate the superiority of DEMTO.