A Novel Multi-Objective Optimization Algorithm Based on Differential Evolution and NSGA-II
Fuqing Zhao, Liu Huan, Yi Zhang, Weimi Ma, Chuck Zhang · 2018
NSGA-II is a well known, fast sorting and elite multi-objective genetic algorithm. The local exploitation ability of NSGA-II is relatively limited by the parameters of crossover and mutation. DE has shown powerful search abilities for continuous optimization. In this paper, an enhanced NSGA-II based on differential evolution and L-near distance (DP-NSGA-II/EDA) is proposed. To improve the diversity and convergence of Pareto optimal solutions by NSGA-II algorithm, DP-NSGA-II/EDA produces two populations by different approaches. One is from NSGA-II itself, the other is from differential evolution (DE). Through the competition between two populations, the superior individuals will be selected to construct new offspring population. Meanwhile, a new distance strategy called L-near distance is introduced to NSGA-II to maintain the diversity of the population. To validate the proposed algorithm, it is compared with the original NSGA-II, SPEA2 and MOEA/D-DE through several numerical benchmark problems. Results show the effectiveness of the proposed approach.