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A cluster-based genetic optimization method for satellite range scheduling system
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DOI：10.1016/j.swevo.2023.101316.png)
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摘要
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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.
Keyword:
Satellite range scheduling
Clustering
Meta-heuristic algorithm
Genetic algorithm
MIP
Task arrangement algorithm
Machine learning
期刊
IF:
8.5
论文数:
2.2K
被引数:
1.0W
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