Optimization of Multiple Debris Removal Missions Using an Evolving Elitist Club Algorithm

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.