基于NSGA-Ⅱ算法的区域覆盖卫星星座优化

Optimization of Regional Coverage Reconnaissance Satellite Constellations by NSGA-Ⅱ Algorithm

针对区域覆盖的卫星星座优化设计,将一种基于Pareto最优概念的快速非支配排序遗传算法(NSGA-Ⅱ)引入区域覆盖侦察卫星星座的多目标优化设计,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VisualC++针对侦察卫星星座进行了仿真,仿真结果表明了算法可以找到多个Pareto解,避免了传统求解方法的权值选择问题,并且较简单遗传算法具有更好的灵活性,为解决星座多目标优化设计问题提供了新的思路。

The paper applys Fast and Elitist Multi -Objective Genetic Algorithm optimal to the optimization of regional coverage reconnaissance satellite constellation （ NSGA - II） based on Pareto The best solution, depending on the importance of different objects, is selected via a kind of multi attributes decision method. The results of the simulation realized by STK and Visual C ＋ ＋ show that the algorithm can get a group of Pareto solutions. The algorithm presented in this paper can avoid to select weight of multiple objects. On the other hand, compared with the simple genetic algorithm, this algorithm is more active. This method provides a new idea for solving the problem of optimization of satellite constellation with multiple objectives .