Abstract
The Earth Observation Satellite Scheduling Problem (EOSSP) is difficult to solve due to its scale and constraints. Through analysing the problem, we build a mathematical programming model of the EOSSP. After that, we propose a reinforcement-learning-driven bees algorithm (RLBA) to solve a large-scale EOSSP (LSEOSSP). The RLBA adopts a Q-learning method to select search operations from global search and neighbourhood search. We define a new state action combination in the Q-learning method to cooperate with the population search and obtain high-quality solutions. Through experimental verification, the performance of the proposed algorithm is obviously better than that of several comparison algorithms, and the RLBA can solve LSEOSSP well.
This work was supported by the Special Projects in Key Fields of Universities in Guangdong (2021ZDZX1019) and the Hunan Provincial Innovation Foundation For Postgraduate (CX20200585).
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Acknowledgement
Junwei Ou and Yanjie Song contribute equally to this article. Thanks to Prof. Cham for his valuable comments.
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Song, Yj., Ou, Jw., Pham, D.T., Li, Jt., Huang, Jb., Xing, Ln. (2023). A Reinforcement-Learning-Driven Bees Algorithm for Large-Scale Earth Observation Satellite Scheduling. In: Pan, L., Zhao, D., Li, L., Lin, J. (eds) Bio-Inspired Computing: Theories and Applications. BIC-TA 2022. Communications in Computer and Information Science, vol 1801. Springer, Singapore. https://doi.org/10.1007/978-981-99-1549-1_7
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