A novel self-adaptive variable-size multi-objective differential evolution algorithm is presented to find the best reconfiguration of existing on-orbit satellites for some particular targets on the ground when an emergent requirement arises in a short period. The main contribution of this study is that three coverage metrics are designed to assess the performance of the reconfiguration. Proposed algorithm utilizes the idea of fixed-length chromosome encoding scheme combined with expression vector and the modified initialization, mutation, crossover and selection operators to search for optimal reconfiguration structure. Multi-subpopulation diversity initialization is adopted first, then the mutation based on estimation of distribution algorithm and adaptive crossover operators are defined to manipulate variable-length chromosomes, and finally a new selection mechanism is employed to generate well-distributed individuals for the next generation. The proposed algorithm is applied to three characteristically different case studies, with the objective to improve the performance with respect to specified targets by minimizing fuel consumption and maneuver time. The results show that the algorithm can effectively find the approximate Pareto solutions under different topological structures. A comparative analysis demonstrates that the proposed algorithm outperforms two other related multi-objective evolutionary optimization algorithms in terms of quality, convergence and diversity metrics. 
Reconfiguration of satellite orbit for cooperative observation using variable-size multi-objective differential evolution