Multi-objective Self-Adaptive Differential Evolution with Dividing Operator and Elitist Archive

A multi-objective self-adaptive differential evolution algorithm with dividing operator and elitist archive is proposed for solving the multi-objective optimization problems. In every generation, the population is divided into two parts randomly and one of the parts will be done by the dividing operator which will enhance the diversity of the population and avoid falling into the local optimal. The numerical experiments implement in four groups: the first group compare the MSDEDE algorithm with five other evolution algorithms; the second group compare the MSDEDE algorithm with NSGA-II, SPEA2 and MOPSO, the simulation results show the effectiveness of the proposed algorithm; the third group compare it with three other DE algorithms, the results show the effectiveness of the proposed self-adaptive method; the fourth group compare it with the multi-objective self-adaptive differential evolution without the dividing operator on five benchmark problems, the results show the proposed dividing operator can improve the convergence speed.