A Hybrid Dynamic Population Genetic Algorithm for Multi-satellite and Multi-station Mission Planning System

Abstract

Satellite is an important space platform today. Achieving reasonable satellite management control greatly affects the development of the aerospace field. Multi-satellite and multi-station mission planning system was proposed to achieve control of satellite resources. In the system, planning algorithms are particularly important, so we proposed a mathematical model for the mission planning of multi-satellite and multi-ground station. Then, we proposed a hybrid dynamic population genetic algorithm (HDPGA) for satellite mission planning. In this algorithm, the large-population size is used for global optimization and the small-population size is used for local improvements. Additionally, the mission planning algorithm (MPA) is used to arrange the mission sequence on the ground station time window. We designed multiple sets of experiments to verify the effect of HDPGA. The results show that our proposed algorithm can meet the needs of the planning system. At the same time, HDPGA is better than the other four algorithms.