中国空间科学技术

中国空间科学技术 ›› 2026, Vol. 46 ›› Issue (1): 73-82.doi: 10.16708/j.cnki.1000-758X.2026.0010

• 低轨巨型星座专题 • 上一篇    下一篇

面向多星协同任务规划的自适应教学优化算法

刘严1,2，刘国华1,2,3,*，温治江1,2，胡海鹰1,2,3   

  1. 1.中国科学院微小卫星创新研究院，上海201306
    2.上海微小卫星工程中心，上海201306
    3.中国科学院大学，北京100039
  • 收稿日期:2025-06-30 修回日期:2025-08-30 录用日期:2025-09-04 发布日期:2026-01-09 出版日期:2026-01-30

Adaptive teaching-learning-based optimization for multi-satellite collaborative mission planning

LIU Yan1,2，LIU Guohua1,2,3,*，WEN Zhijiang1,2，HU Haiying1,2,3   

  1. 1.Innovation Academy for Microsatellites of Chinese Academy of Sciences, Shanghai 201306,China
    2.Shanghai Engineering Center for Microsatellites，Shanghai 201306，China
    3.University of Chinese Academy of Sciences, Beijing 100039, China
  • Received:2025-06-30 Revision received:2025-08-30 Accepted:2025-09-04 Online:2026-01-09 Published:2026-01-30

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摘要： 针对低轨大规模星座协同观测任务规划中动态适应性不足的问题，提出一种自适应教学优化算法。在教学优化算法的教与学框架下通过引入自适应机制和混合学习策略，采用时变教学因子和精英导向机制优化教阶段，采用混合学习策略改进学阶段，动态平衡全局探索与局部探索能力。通过仿真验证，自适应教学优化算法在任务完成率和运行时间上均优于改进遗传算法和改进差分教学优化算法，在大规模高复杂度多星协同任务场景下相对基线算法任务完成率可提升6%与16%，适用于高维离散优化问题。算法在任务完成率、运行效率及鲁棒性上具有综合优势，可应用于低轨星座协同观测任务。

关键词: 敏捷卫星, 任务规划, 多点目标, 对地观测, 在轨规划, 教学优化算法

Abstract: To address the insufficient dynamic adaptability in collaborative observation mission planning for low-earth orbit mega-constellations, an adaptive teaching-learning-based optimization algorithm was proposed. Within the teaching-learning framework, adaptive mechanisms and hybrid learning strategies were incorporated. The teaching phase was enhanced through time-varying teaching factors and an elite-guided mechanism, while the learning phase was improved using hybrid learning strategies to dynamically balance global exploration and local exploitation capabilities. Simulations demonstrated that the proposed algorithm outperformed both the improved genetic algorithm and the improved differential teaching-learning-based optimization algorithm in terms of task completion rate and computational time. In large-scale, high-complexity multi-satellite collaborative mission scenarios, it achieved 6% and 16% higher task completion rates compared to baseline algorithms, proving suitable for high-dimensional discrete optimization problems. The algorithm exhibits advantages in task completion rate, operational efficiency, and robustness, making it applicable to collaborative observation missions in low-earth orbit constellations.

Key words: agile satellite, mission planning, multi-point targets, earth observation, on-orbit planning, teaching-learning-based optimization algorithm