Space debris has become a great threat to space activities. We study and optimize space missions to remove multiple space debris. These missions are efficient and economical in stabilizing the orbital environment. In this paper, we propose an intelligent global optimization algorithm named the evolving elitist club algorithm (EECA), and we study its application in multiple debris removal missions. The crucial design element of EECA is the elitist club, which is a population created using ant colony optimization (ACO) and then evolved using genetic algorithm operators. We describe two types of multiple debris removal missions: 1) single spacecraft removal mission; and 2) multiple spacecraft removal mission. We optimize them using EECA. The proposed algorithm is compared with the conventional tree search method and other ACO algorithms. We show that EECA is more efficient than other algorithms. Furthermore, a time distribution (TD) EECA is proposed to handle time-dependent problems, and the time-continuous update mechanism of time-varying pheromone in TD EECA presents a novel idea for combinatorial-continuous problems.

中文翻译：

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使用进化的精英俱乐部算法优化多个碎片清除任务

空间碎片已成为空间活动的巨大威胁。我们研究和优化太空任务以清除多个太空碎片。这些任务在稳定轨道环境方面既高效又经济。在本文中，我们提出了一种名为进化精英俱乐部算法（EECA）的智能全局优化算法，并研究了其在多个碎片清除任务中的应用。EECA 的关键设计元素是精英俱乐部，它是使用蚁群优化 (ACO) 创建然后使用遗传算法算子进化的种群。我们描述了两种类型的多碎片清除任务：1）单个航天器清除任务；2) 多个航天器拆除任务。我们使用 EECA 优化它们。将所提出的算法与传统的树搜索方法和其他ACO算法进行了比较。我们表明 EECA 比其他算法更有效。此外，提出了时间分布 (TD) EECA 来处理时间相关问题，TD EECA 中时变信息素的时间连续更新机制为组合连续问题提供了一种新颖的思路。