A cluster-based genetic optimization method for satellite range scheduling system

With the rapid development of the satellite industry, how to effectively manage satellites has become an essential issue for ground operation management. By using the k-means clustering method, a cluster-based genetic algorithm (C-BGA) is proposed for the satellite ranging scheduling problem (SRSP). In the C-BGA, a heuristic-based population initialization strategy and a cluster-based evolution strategy are designed for searching for an ideal solution. Four heuristic rules were used in the initial population generation process. Population evolution process is accomplished by cluster-based crossover and mutation. These strategies also improve the algorithm's adaptability to cope with different scenarios. To increase the possibility of the task being successfully scheduled, a task arrangement algorithm (TAA) is used to generate task execution plans. Experiments are carried out to prove that the proposed algorithm can effectively solve the SRSP problem.