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Volume 8 Issue 11
Nov.  2021

IEEE/CAA Journal of Automatica Sinica

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Yonghao Du, Ling Wang, Lining Xing, Jungang Yan and Mengsi Cai, "Data-Driven Heuristic Assisted Memetic Algorithm for Efficient Inter-Satellite Link Scheduling in the BeiDou Navigation Satellite System," IEEE/CAA J. Autom. Sinica, vol. 8, no. 11, pp. 1800-1816, Nov. 2021. doi: 10.1109/JAS.2021.1004174
Citation: Yonghao Du, Ling Wang, Lining Xing, Jungang Yan and Mengsi Cai, "Data-Driven Heuristic Assisted Memetic Algorithm for Efficient Inter-Satellite Link Scheduling in the BeiDou Navigation Satellite System," IEEE/CAA J. Autom. Sinica, vol. 8, no. 11, pp. 1800-1816, Nov. 2021. doi: 10.1109/JAS.2021.1004174

Data-Driven Heuristic Assisted Memetic Algorithm for Efficient Inter-Satellite Link Scheduling in the BeiDou Navigation Satellite System

doi: 10.1109/JAS.2021.1004174
Funds:  This work was supported by the National Natural Science Foundation of China (61773120), the National Natural Science Fund for Distinguished Young Scholars of China (61525304), the Foundation for the Author of National Excellent Doctoral Dissertation of China (2014-92), the Hunan Postgraduate Research Innovation Project (CX2018B022), and the China Scholarship Council-Leiden University Scholarship
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  • Inter-satellite link (ISL) scheduling is required by the BeiDou Navigation Satellite System (BDS) to guarantee the system ranging and communication performance. In the BDS, a great number of ISL scheduling instances must be addressed every day, which will certainly spend a lot of time via normal metaheuristics and hardly meet the quick-response requirements that often occur in real-world applications. To address the dual requirements of normal and quick-response ISL schedulings, a data-driven heuristic assisted memetic algorithm (DHMA) is proposed in this paper, which includes a high-performance memetic algorithm (MA) and a data-driven heuristic. In normal situations, the high-performance MA that hybridizes parallelism, competition, and evolution strategies is performed for high-quality ISL scheduling solutions over time. When in quick-response situations, the data-driven heuristic is performed to quickly schedule high-probability ISLs according to a prediction model, which is trained from the high-quality MA solutions. The main idea of the DHMA is to address normal and quick-response schedulings separately, while high-quality normal scheduling data are trained for quick-response use. In addition, this paper also presents an easy-to-understand ISL scheduling model and its NP-completeness. A seven-day experimental study with 10 080 one-minute ISL scheduling instances shows the efficient performance of the DHMA in addressing the ISL scheduling in normal (in 84 hours) and quick-response (in 0.62 hour) situations, which can well meet the dual scheduling requirements in real-world BDS applications.


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    • A well-designed framework to address both normal and quick-response ISL scheduling
    • A high-performance memetic algorithm via multi-strategies for normal ISL scheduling
    • A quick data-driven heuristic via probability prediction for quick ISL scheduling
    • An easy-to-understand ISL scheduling model and its NP-completeness
    • Efficient performance of our algorithm in a seven-day experiment (10,080 instances)


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