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基于分解优化的多星合成观测调度算法
引用本文:白保存,陈英武,贺仁杰,李菊芳.基于分解优化的多星合成观测调度算法[J].自动化学报,2009,35(5):596-604.
作者姓名:白保存  陈英武  贺仁杰  李菊芳
作者单位:1.国防科技大学信息系统与管理学院 长沙 410073
摘    要:某些卫星的侧摆性能较差, 必须进行合成观测以提高观测效率. 研究了多星联合对地观测中的任务合成观测调度问题. 提出了将原问题分解为任务分配与任务合成的分解优化思路. 任务分配为任务选择卫星资源及时间窗口; 任务合成则针对该分配方案,将分配到各卫星的任务按照轨道圈次分组, 分别进行最优合成. 采用蚁群优化算法(Ant colony optimization, ACO)求解任务分配问题, 通过自适应参数调整及信息素平滑策略, 实现全局搜索和快速收敛间的平衡.提出了基于动态规划的最优合成算法, 求解任务合成子问题,能够在多项式时间内求得最优合成方案. 依据分配方案的合成结果, 得到优化方案的特征信息, 反馈并引导蚁群优化算法对任务分配方案的搜索过程. 大规模测试算例验证了本文算法的效率.

关 键 词:遥感卫星    调度    任务合成    分解优化    自适应蚁群算法    动态规划
收稿时间:2008-1-23
修稿时间:2008-6-30

Scheduling Satellites Observation and Task Merging Based on Decomposition Optimization Algorithm
BAI Bao-Cun CHEN Ying-Wu HE Ren-Jie LI Ju-Fang.College of Information System , Management,National University of Defense Technology,Changsha.Scheduling Satellites Observation and Task Merging Based on Decomposition Optimization Algorithm[J].Acta Automatica Sinica,2009,35(5):596-604.
Authors:BAI Bao-Cun CHEN Ying-Wu HE Ren-Jie LI Ju-FangCollege of Information System  Management  National University of Defense Technology  Changsha
Affiliation:1.College of Information System and Management, National University of Defense Technology, Changsha 410073
Abstract:The problem of satellites observation scheduling and task merging is investigated. The problem is divided into two sub-problems: task assignment problem and task merging problem. For the task assignment phase, we propose an adaptive ant colony optimization (ACO) algorithm to select the specific satellite and the specific time window for each task. Adaptive parameter adjusting and pheromone trail smoothing strategies are introduced to balance the exploration and the exploitation of search. For the task merging phase, a polynomial optimization algorithm based on dynamic programming is developed to find the best merging solution. The result of task merging is feedback to the ant colony, which can guide the search process of ant colony optimization algorithm. Computation results demonstrate the effectiveness of our algorithm.
Keywords:Remote sensing satellite  scheduling  task merging  decomposition optimization  adaptive ant colony optimization  dynamic programming
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