引用本文:宋彦杰,王沛,张忠山,邢立宁,陈英武.面向多星任务规划问题的改进遗传算法[J].控制理论与应用,2019,36(9):1391~1397.[点击复制]
SONG Yan-jie,WANG Pei,ZHANG Zhong-shan,XING Li-ning,CHEN Ying-wu.An improved genetic algorithm for multi-satellite mission planning problem[J].Control Theory and Technology,2019,36(9):1391~1397.[点击复制]
面向多星任务规划问题的改进遗传算法
An improved genetic algorithm for multi-satellite mission planning problem
摘要点击 1850  全文点击 805  投稿时间：2018-12-04  修订日期：2019-04-22
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DOI编号  10.7641/CTA.2019.80957
  2019,36(9):1391-1397
中文关键词  卫星  规划  遗传算法  优化  算法
英文关键词  satellite  planning  genetic algorithms  optimization  algorithm
基金项目  国家自然科学基金
作者单位E-mail
宋彦杰 国防科技大学 songyj_2017@163.com 
王沛 部队  
张忠山 国防科技大学  
邢立宁* 国防科技大学 xing2999@qq.com 
陈英武 国防科技大学  
中文摘要
      卫星数量的快速增加为管控卫星的工作增加了很大的难度，如何有效地进行任务规划，有效管理卫星资源，成为了卫星领域的一个重要问题.针对此问题, 本文构建了多星任务规划的数学模型, 将最大化任务收益作为优化目标. 本文分析了问题的难点并提出了一种包含两种优化策略的改进遗传算法，包括全局优化和局部优化两部分. 全局优化和局部优化根据种群改进情况进行自适应切换. 通过两种优化方法的结合可以提升任务规划的效果.本文还提出了一种任务规划算法，用于为改进遗传算法得到的任务序列选择合适的任务执行时间. 仿真实验证明本文提出的改进遗传算法可以很好地解决多星任务规划问题,与对比算法相比可以得到更优的规划结果. 改进遗传算法有很好的工程应用前景.
英文摘要
      Rapid increase in the number of satellites has greatly increased the difficulty of managing satellites. How to effectively plan missions and effectively manage satellite resources has become an important issue in the satellite field. In this paper, a mathematical model of multi-satellite mission planning was constructed, which maximizes mission profit as an optimization goal. Difficulties of the problem were analyzed an improved genetic algorithm of optimization strategy was proposed, which includes two parts: global optimization and local optimization. Global optimization and local optimization are adaptively switched according to the population improvement. Combination of two optimization methods can improve the effect of task planning. A task scheduling algorithm for selecting the appropriate task execution time for the task sequence obtained by genetic algorithm was also proposed. Simulation experiment proves that the improved genetic algorithm proposed can solve the multi-satellite mission planning problem well. Compared with the comparison algorithm, better planning results can be obtained. The improved genetic algorithm has a good engineering application prospect.