引用本文:张广辉,魏晨轩,冯彦翔,李晓玲.基于混合进化博弈的多敏捷卫星多目标调度优化[J].控制理论与应用,2025,42(11):2341~2351.[点击复制]
ZHANG Guang-hui,WEI Chen-xuan,FENG Yan-xiang,LI Xiao-ling.Multi-agile satellites multi-objective scheduling based on hybrid evolutionary game theory[J].Control Theory & Applications,2025,42(11):2341~2351.[点击复制]
基于混合进化博弈的多敏捷卫星多目标调度优化
Multi-agile satellites multi-objective scheduling based on hybrid evolutionary game theory
摘要点击 2264  全文点击 116  投稿时间：2025-02-09  修订日期：2025-11-12
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DOI编号  10.7641/CTA.2025.50050
  2025,42(11):2341-2351
中文关键词  敏捷卫星  任务调度  时间依赖性  多目标  进化博弈  自学习
英文关键词  agile satellite  task scheduling  time-dependent  multi-objective  evolutionary game  self-learning
基金项目  河北省自然科学基金项目(F2024204007),西安交通大学机械制造系统工程国家重点实验室开放课题项目(sklms2023002)资助.
作者单位E-mail
张广辉* 河北农业大学信息科学与技术学院 ghzhang@hebau.edu.cn 
魏晨轩 河北农业大学信息科学与技术学院  
冯彦翔 西安交通大学自动化科学与工程学院  
李晓玲 长安大学电子与控制工程学院  
中文摘要
      随着航天技术的不断发展,具有先进姿态机动能力的敏捷地球观测卫星在气候监测、军事动态等方面发挥 重要作用.为满足复杂的敏捷卫星任务调度需求,本文以最小化观测任务失败率和卫星负载均衡为目标,研究了带 有时间依赖性的多敏捷卫星多目标调度优化问题.首先,基于问题特征构建了数学规划模型.其次,基于进化博弈 理论提出一种混合进化博弈调度算法(HEGSA),包括全局开采和局部勘探两个进化阶段,全局开采阶段通过启发式 策略生成具有异构身份的两个子种群,并采用多目标进化博弈策略优化每个子种群以平衡收敛性和多样性;局部 勘探阶段采用一种自学习算子以增强对解空间的高效搜索.最后,通过仿真实验验证了HEGSA的有效性.
英文摘要
      The continuous advancement of aerospace technology has enabled agile earth observation satellites with advanced attitude maneuverability to play a significant role in climate monitoring, military intelligence, and other fields. In order to meet the complex agile satellite task scheduling requirements, this paper investigates a time-dependent multi objective scheduling optimization problem for multiple agile satellites, aiming to minimize task failure rates and satellite load balancing. Firstly, a mathematical programming model is constructed based on the problem characteristics. Secondly, a hybrid evolutionary game scheduling algorithm (HEGSA) is proposed based on evolutionary game theory, which includes two evolutionary stages: global exploitation and local exploration. In the global exploitation stage, heuristic strategies are employed to generate two subpopulations with heterogeneous identities, and a multi-objective evolutionary game strategy is used to optimize each subpopulation to balance convergence and diversity. In the local exploration stage, a self-learning operator is used to enhance the efficient search of the solution space. Finally, the effectiveness of HEGSA is verified through simulation experiments.