基于多种群混沌遗传算法的GEO目标服务任务规划

Aiming to address diverse on-orbit service requirements, such as debris removal and fuel refueling in geosynchronous Earth orbit (GEO). the problem of spacecraft mission scheduling combining “fixed fuel station + round-trip spacecraft” is investigated. Firstly, a fuel-optimal bi-level mission scheduling model with a multi-mission hybrid is established, in which the outer layer is designed for target service sequence scheduling and the inner layer is designed orbit maneuver planning. Then, for this continuous-discrete mixed variable combinatorial optimization problem, a multi-group chaotic genetic algorithm (MGCGA) is proposed, in which the hybrid coding is employed to represent the decision variables and a cubic chaotic mapping operator is introduced to improve the quality of the initial population. Moreover, a multi-group and elite retention strategy is employed to significantly approach the optimal global solution during the solution process. Finally, a typical scenario is constructed using actual GEO target information. The scheduling results show that the proposed algorithm has the advantages of good global convergence and fast convergence.