Recently, versions of the Genetic Algorithm (GA) have successfully generated low-Earth orbit sparse coverage satellite constellations that appear to outperform traditionally developed constellations. The objective of these constellations was to minimize the maximum revisit time over a latitude band of interest. However, many constellation designers are also concerned with the average revisit time, and contrary to expectations, these two objectives often compete with each other. This paper presents a multiobjective GA approach to generate numerous constellation designs that show the trade-off between the revisit time objectives. These trade studies are conducted using a single run of the multiobjective GA. The designs generated using this approach are discussed and some trends are examined.

中文翻译：

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使用多目标遗传算法的卫星星座平均和最大重访时间交易研究

最近，遗传算法（GA）的版本已成功生成了看起来比传统开发的星座更好的低地球轨道稀疏覆盖卫星星座。这些星座的目的是在感兴趣的纬度带上将最大重访时间最小化。但是，许多星座设计者也关心平均重访时间，并且与预期相反，这两个目标经常相互竞争。本文提出了一种多目标遗传算法来生成众多星座图，这些星座图表明了重访时间目标之间的权衡。这些贸易研究是通过单次运行的多目标GA进行的。讨论了使用这种方法生成的设计，并研究了一些趋势。