A Fast Memetic Multi-objective Differential Evolution for Multi-tasking Optimization

Multi-tasking optimization has now become a promising research topic that has attracted increasing attention from researchers. In this paper, an efficient memetic evolutionary multi-tasking optimization framework is proposed. The key idea is to use multiple subpopulations to solve multiple tasks, with each subpopulation focusing on solving a single task. A knowledge transferring crossover is proposed to transfer knowledge between subpopulations during the evolution. The proposed framework is further integrated with a multi-objective differential evolution and an adaptive local search strategy, forming a memetic multi-objective DE named MM-DE for multi-tasking optimization. The proposed MM-DE is compared with the state-of-the-art multi-tasking multi-objective evolutionary algorithm (named MOMFEA) on nine benchmark problems in the CEC 2017 multi-tasking optimization competition. The experimental results have demonstrated that the proposed MM-DE can offer very promising performance.