基于多目标进化算法的低轨区域通信星座优化设计

研究利用进化算法实现低轨区域通信星座的多目标优化设计。首先分析、确定低轨星座优化的轨道控制参数，然后将基于Pareto最优概念的多目标进化算法引入星座优化中。结合星座覆盖性能的评价准则，给出了一种利用非劣分层遗传算法(NSGA—Ⅱ)实现星座轨道控制参数优化的框架。最后对具体实例进行了优化仿真，结果表明，该方法可以获得一组分布合理的Pareto解，为星座方案决策提供了有力的支持。

Multi -object optimization of LEO regional communication satellite constellation with GA algorithm

A multi-object optimization of LEO regional communication constellation based on genetic algorithm was studied. First the orbit control parameters for LEO constellation optimization were analyzed and determined; then multi-object genetic algorithm based on Pareto maximum was introduced into constellation optimization. Combining the coverage assessment criterions, an orbit parameters optimization framework based on nondominated sorting genetic algorithm (NSGA-II) was proposed. This method is applied to a detailed example, and result shows a group of Pareto solutions with good spread can be achieved, which gives strong support to constellation scheme determination.