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.
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.
出处
《自动化学报》
EI
CSCD
北大核心
2009年第5期596-604,共9页
Acta Automatica Sinica
基金
国家自然科学基金(70601035,70801062)资助~~
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
Remote sensing satellite, scheduling, task merging, decomposition optimization, adaptive ant colony optimization, dynamic programming
