Agile earth observing satellites mission planning using genetic algorithm based on high quality initial solutions

This paper presents an improved genetic algorithm to solve the agile earth observing satellite mission planning problem. We study how to rapidly generate high quality initial solutions, and four generation strategies are proposed. The effect of the settings of operator parameters on the performance of the algorithm is analyzed. The experiment results show that the genetic algorithm based on high quality initial solutions generated by Hybrid Random Heuristic Strategy (HRHS) is more effective in solving the agile satellite mission planning problem, but in a certain time cost. We expect that our results will provide insights for the future application of genetic algorithm to satellites mission planning problems.