A novel two-level optimization strategy for multi-debris active removal mission in LEO

Recent studies of the space debris environment in Low Earth Orbit (LEO) have shown that the critical density of space debris has been reached in certain regions. The Active Debris Removal (ADR) mission, to mitigate the space debris density and stabilize the space debris environment, has been considered as a most effective method. In this paper, a novel two-level optimization strategy for multi-debris removal mission in LEO is proposed, which includes the low-level and high-level optimization process. To improve the overall performance of the multi-debris active removal mission and obtain multiple Pareto-optimal solutions, the ADR mission is seen as a Time-Dependant Traveling Salesman Problem (TDTSP) with two objective functions to minimize the total mission duration and the total propellant consumption. The problem includes the sequence optimization to determine the sequence of removal of space debris and the transferring optimization to define the orbital maneuvers. Two optimization models for the two-level optimization strategy are built in solving the multi-debris removal mission, and the optimal Pareto solution is successfully obtained by using the non-dominated sorting genetic algorithm II (NSGA-II). Two test cases are presented, which show that the low level optimization strategy can successfully obtain the optimal sequences and the initial solution of the ADR mission and the high level optimization strategy can efficiently and robustly find the feasible optimal solution for long duration perturbed rendezvous problem.