A Time Window Sequence-Based Evolutionary Algorithm for Solving Large-Scale Daily Task Planning Problems

The daily task planning problem (DTPP) in the ground control of satellites has been a significant focus for space scientists. Although various optimization methods have been devoted to solving this problem, most of them can only deal with simple DTPPs with a few variables and naive objectives, which cannot obtain multiple optimal solutions that meet the complex requirements of real-world scenarios. To address this issue, this paper formulates DTPPs into a large-scale multi-objective optimization problem and solves it using an evolutionary algorithm embedded with heuristic strategies. The proposed algorithm can quickly approach the optimal solution of DTPPs and can be seamlessly embedded into various evolutionary algorithms. In order to evaluate the performance of the proposed algorithm, statistical experiments are carried out on the DTPP with nine real-world datasets. The experimental results demonstrate that the proposed algorithm has significant advantages over several representative evolutionary algorithms.