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Modeling and Solving for Multi-Satellite Cooperative Task Allocation Problem Based on Genetic Programming Method

Author

Listed:
  • Weihua Qi

    (Department of System Engineering, National University of Defense Technology, Changsha 410073, China)

  • Wenyuan Yang

    (Department of System Engineering, National University of Defense Technology, Changsha 410073, China)

  • Lining Xing

    (Department of System Engineering, National University of Defense Technology, Changsha 410073, China)

  • Feng Yao

    (Department of System Engineering, National University of Defense Technology, Changsha 410073, China)

Abstract

The past decade has seen an increase in the number of satellites in orbit and in highly dynamic satellite requests, making the control by ground stations inefficient. The traditional management composed of ground planning with separate onboard execution is seriously lagging in response to dynamically incoming tasks. To meet the demand for the real-time response to emergent events, a multi-autonomous-satellite system with a central-distributed collaborative architecture was formulated by an integer programming model. Based on the structure, evolutionary rules were proposed to solve this problem by the use of sequence solution construction and a constructed heuristic method based on gene expression programming evolution. First, the features of the problem are extracted based on domain knowledge, then, the problem-solving rules are evolved by gene expression programming. The simulation results reflect that the evolutionary rule completely surpasses the three types of heuristic rules with adaptive mechanisms and achieves a solution effect close to meta-heuristic algorithms with a reasonably fast solving speed.

Suggested Citation

  • Weihua Qi & Wenyuan Yang & Lining Xing & Feng Yao, 2022. "Modeling and Solving for Multi-Satellite Cooperative Task Allocation Problem Based on Genetic Programming Method," Mathematics, MDPI, vol. 10(19), pages 1-21, October.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:19:p:3608-:d:932026
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    References listed on IDEAS

    as
    1. Gurkan Ozturk & Ozan Bahadir & Aydin Teymourifar, 2019. "Extracting priority rules for dynamic multi-objective flexible job shop scheduling problems using gene expression programming," International Journal of Production Research, Taylor & Francis Journals, vol. 57(10), pages 3121-3137, May.
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