电子电气工程与控制

一种基于偏好MOEA的卫星地面站资源多目标优化算法

  • 孙刚 ,
  • 陈浩 ,
  • 彭双 ,
  • 杜春 ,
  • 李军
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  • 国防科技大学 电子科学学院, 长沙 410073

收稿日期: 2020-07-02

  修回日期: 2020-07-22

  网络出版日期: 2021-04-30

基金资助

国家自然科学基金(61806211,U19A2058);湖南省自然科学基金(2020 JJ4103)

Multi-objective optimization algorithm for satellite range scheduling based on preference MOEA

  • SUN Gang ,
  • CHEN Hao ,
  • PENG Shuang ,
  • DU Chun ,
  • LI Jun
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  • College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China

Received date: 2020-07-02

  Revised date: 2020-07-22

  Online published: 2021-04-30

Supported by

National Natural Science Foundation of China (61806211,U19A2058);Natural Science Foundation of Hunan Province (2020 JJ4103)

摘要

随着中国航天事业的发展,卫星地面站资源匮乏问题日益突出,需要对其进行统筹优化使用。因此,卫星地面站资源规划问题得到了广泛关注。在分析问题特点的基础上,对用户规划结果的偏好信息进行建模表达,建立了涵盖用户偏好的多目标数学规划模型,提出了基于偏好多目标进化算法的卫星地面站资源规划算法。为了进一步提升算法性能,设计了基于领域知识的启发式策略,包括:任务扩充策略、冲突消解策略以及任务缩减策略等。实验结果表明,与现有算法相比,用户偏好信息的引入能有效提升问题求解针对性,在IGD-CF (Inverted Generational Distance based on Composite Front)指标上取得了更好的效果。

本文引用格式

孙刚 , 陈浩 , 彭双 , 杜春 , 李军 . 一种基于偏好MOEA的卫星地面站资源多目标优化算法[J]. 航空学报, 2021 , 42(4) : 524475 -524475 . DOI: 10.7527/S1000-6893.2020.24475

Abstract

Progress in China’s aerospace technologies and applications makes the satellite ground station resource shortage problem increasingly prominent. It is necessary to optimize the usage of satellite ground station resources, also called satellite range scheduling problem, which has received extensive attention. Based on the analysis of the characteristics of the problem, the user's preference information on the scheduling results is modeled and formulated, a multi-objective mathematical scheduling model covering the user's preferences is established, and a satellite range scheduling algorithm based on preference-based multi-objective evolutionary algorithm is proposed. To further improve the performance of the proposed algorithm, heuristic strategies based on domain-knowledge including the tasks expansion strategy, conflicts resolution strategy and tasks reduction strategy are designed. Experimental results show that, with the help of user’s preference information, the proposed algorithm can effectively improve the capacity of exploring solutions in the preference region, outweighing the-state-of-art algorithm in the Inverted Generational Distance based on Composite Front(IGD-CP) indicator.

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