A differential evolution-based hybrid NSGA-II for multi-objective optimization

To improve the search accuracy and diversity of non-dominated sorting genetic algorithm (NSGA-II), an improved algorithm DMNSGA-II referencing to the strategy of differential evolution to strengthen local search is proposed in this paper. The algorithm uses mutation guiding operator and crossover operator of DE to replace crossover operator in NSGA-II to enhance the local search capability and improve search accuracy. while retaining the mutation operator of NSGA-II to improve diversity. We use four benchmark test problems to investigate the performance of the DMNSGA-II algorithm, and simulation results demonstrate that the proposed algorithm can achieve a good overall performance in multi-objective optimization.