关键词: 多目标优化, NSGA-II算法, 反向学习, 卫星星座

Abstract: In order to overcome the shortages of Simulated Binary Crossover（SBX） operator, convergence speed and population diversity of NSGA-II, this paper applies the opposition-based learning mechanism?to the initialization and evolution process of NSGA-II algorithm. In addition, the paper introduces an improved arithmetic crossover operator as well. The convergence and diversity of the proposed algorithm on the series of ZDT test benchmarks are evaluated?and the results show that the improved NSGA-II algorithm is better than the traditional NSGA-II on converge speed, convergence and diversity. The paper applies the proposed algorithm to the optimization of satellite constellation design and the results indicate that the improved algorithm is very effective on this application.

Key words: multi-objective optimization, NSGA-II, opposition-based learning, satellite constellation