北京信息科技大学学报（自然科学版）2026，Vol.41Issue(4)：1-12,12.DOI:10.16508/j.cnki.11-5866/n.2026.04.001
基于PPO策略选择引导灰狼优化的敏捷卫星任务规划算法
An agile satellite mission planning algorithm based on PPO strategy-selection guided grey wolf optimization
摘要
Abstract
With the continuous growth in demand for timely and high-precision earth observation,agile satellite mission planning faces significant challenges in large-scale scenarios involving highly complex constraints.To address these issues,an agile satellite mission planning algorithm based on grey wolf optimization guided by proximal policy optimization(PPO)strategy selection was proposed.In this algorithm,the grey wolf optimizer was employed to generate multiple candidate scheduling strategies and construct a scale-controlled candidate set through rapid evaluation.On this basis,PPO was introduced to adaptively select strategies,forming a hybrid optimization framework that integrates heuristic generation with reinforcement learning-based decision making.To accommodate large-scale scenarios,a sparse action space was constructed through candidate screening to reduce decision complexity while preserving effective exploration capability.Experimental results demonstrate that the proposed algorithm outperforms A-ALNS,GRILS,and RL-GA algorithms in both computational efficiency and task return rate,validating its effectiveness in large-scale agile satellite mission planning.关键词
敏捷卫星/任务规划/灰狼优化算法/强化学习Key words
agile satellite/mission planning/grey wolf optimizer/reinforcement learning分类
航空航天引用本文复制引用
汪晨,曹林,王金晓,高鹏飞,宋沛然,杜康宁..基于PPO策略选择引导灰狼优化的敏捷卫星任务规划算法[J].北京信息科技大学学报（自然科学版）,2026,41(4):1-12,12.基金项目
国家自然科学基金项目(U20A20163,62201066) （U20A20163,62201066）
目标认知与应用技术重点实验室开放基金项目(2023-CXPT-LC-005) （2023-CXPT-LC-005）
北京市自然科学基金项目(4264103) （4264103）
