航空学报 > 2023, Vol. 44 Issue (12): 327759-327759   doi: 10.7527/S1000-6893.2022.27759

基于自适应模拟退火的大规模星座测控资源调度算法

伍国华(), 王天宇   

  1. 中南大学 交通运输与工程学院,长沙 410073
  • 收稿日期:2022-07-04 修回日期:2022-07-20 接受日期:2022-08-04 出版日期:2022-08-09 发布日期:2022-08-08
  • 通讯作者: 伍国华 E-mail:guohuawu@csu.edu.cn
  • 基金资助:
    国家自然科学基金(62073341)

Large-scale constellation TT&C resource scheduling algorithm based on adaptive simulated annealing

Guohua WU(), Tianyu WANG   

  1. School of Traffic and Transportation Engineering,Central South University,Changsha 410073,China
  • Received:2022-07-04 Revised:2022-07-20 Accepted:2022-08-04 Online:2022-08-09 Published:2022-08-08
  • Contact: Guohua WU E-mail:guohuawu@csu.edu.cn
  • Supported by:
    National Natural Science Foundation of China(62073341)

摘要:

随着在轨卫星数量快速增长,使得本就相对匮乏的测控(TT&C)资源更加紧张。面向大规模星座的测控资源调度问题受到多重约束条件的限制,是一个冲突性很强的复杂组合优化问题。为提高卫星测控系统的测控能力和测控任务的完成收益,提出一种改进的自适应模拟退火算法以解决大规模星座测控资源调度问题。首先分析卫星测控资源调度问题的业务流程,针对卫星测控任务需求和资源使用约束,建立了以最大化任务完成总收益为优化目标的约束满足模型,并且提出一种评估任务选择可用测控弧段冲突度的方法,设计了基于适应度的任务分配算法以生成一个质量较优的初始可行解。然后设计了结合扰动策略和禁忌表的自适应模拟退火算法,即在算法迭代过程中自适应的控制温度和邻域结构选择概率的更新,通过禁忌表的短期记忆机制避免重复搜索,结合扰动策略对解进行一定的扰动,从而跳出局部最优解,增强算法的寻优性能。最后为验证所提出算法的有效性,开展大量的仿真实验,将该算法与模拟退火算法、遗传算法、基于适应度的任务分配算法和最大权重最先分配算法进行对比。实验结果表明:所提算法相比传统算法在任务收益率方面分别提高了10.34%、23.59%、23.20%和46.51%。

关键词: 大规模星座, 测控资源调度, 冲突度, 模拟退火算法, 自适应

Abstract:

With the rapid growth of the number of satellites in orbit, the already relatively scarce Tracking Telemetry and Command (TT&C) resources are becoming even more scarce. The TT&C resource scheduling problem for large-scale constellation is restricted by multiple constraints, and is a complex combinatorial optimization problem with strong conflicts. An improved adaptive simulated annealing algorithm is proposed to solve the TT&C resource scheduling problem for large-scale constellation in order to improve the capability of TT&C systems and the revenue from completing TT&C tasks. Firstly, the satellite TT&C process is analyzed. According to satellite TT&C task requirements and resource usage constraints, a constraint satisfaction model is established aiming to maximize the total profits from task completion. A method for evaluating the conflict degree of available TT&C opportunities is proposed, and a fitness based task allocation algorithm is designed to generate a high-quality initial solution. Then, an adaptive simulated annealing algorithm combining perturbation strategy and tabu mechanism is designed, which adaptively controls the update of temperature and neighborhood structure selection probability during the optimization process of the algorithm. The short-term memory mechanism of the tabu table is used to avoid repeated searches. The perturbation strategy is combined to diverse the solution to a certain extent, thereby jumping out of the local optima and enhancing the optimization performance of the algorithm. Finally, in order to verify the effectiveness of the proposed method, a large number of simulation experiments were conducted to compare the algorithm with simulated annealing algorithm, genetic algorithm, fitness based task allocation algorithm, and maximum weight first allocation algorithm. Experimental results show that compared with traditional algorithms, the proposed algorithm improves the solution by 10.34%, 23.59%, 23.20%, and 46.51%, respectively.

Key words: large-scale constellation, TT&C resource scheduling, conflict degree, simulated annealing algorithm, self-adaptive

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