基于改进的NSGA-Ⅱ算法的区域覆盖卫星星座优化

OPTIMIZATION OF REGIONAL COVERAGE SATELLITE CONSTELLAION BY IMPROVED NSGA-Ⅱ ALGORITHM

对基于Pareto最优概念的非劣性分层遗传算法(NSGA-Ⅱ)进行了改进,与区域覆盖卫星星座的多目标优化设计相结合,提出基于改进的NSGA-Ⅱ算法的区域覆盖卫星星座优化设计方法,并利用多属性决策中的字典序法,根据目标的重要程度,在得到的Pareto解中进行选择.最后,利用STK和Mat1ab工具对遥感卫星星座进行了仿真,仿真结果表明该算法可以找到多个Pareto解,避免了传统的多目标优化求解方法的权值选择问题,并且比简单遗传算法具有更好的灵活性,从而解决了多目标优化的星座设计问题.

This paper presents a new method to design regional coverage satellite constellation, the non-dominated sorting genetic algorithm Ⅱ (NSGA-Ⅱ) based on Pareto optimal is improved and applied it to the optimization of regional coverage satellite constellation. The best solution,depending on the importance of different objects, is selected by a kind of multi attributes decision making method. Simulations on remote sensing satellite constellation are presented. The results of the simulation realized by STK and Matlab show that the algorithm can get a group of Pareto solutions. The algorithm presented in this paper can avoid selecting weights of multiple objects. On the other hand, compared with the simple genetic algorithm, our algorithm is more active. Thus the question of optimization of satellite constellation with multiple objectives can be solved.