摘要
提出了一种求解多星成像调度问题的基于分解的优化算法,将问题分解为任务分配主问题与单星成像调度子问题.任务分配主问题生成不同卫星的任务分配方案,单星成像调度子问题则根据分配的任务进行优化,生成每颗卫星的成像调度方案.采用自适应的蚁群算法求解任务分配主问题,通过自适应参数调整策略及信息素平滑策略,实现全局搜索和快速收敛间的平衡.采用启发式算法及快速模拟退火算法求解单星成像调度子问题,通过综合多颗卫星的调度结果,可以对任务分配方案进行评价,引导蚁群算法搜索优化的任务分配方案,最终得到多颗卫星的成像调度方案.大规模测试算例验证了算法的效率.
An optimization algorithm based on decomposition is proposed for solving satellites observation scheduling problem. The problem is decomposed into task assignment main problem and single satellite scheduling sub-problem. In task assignment phase, the tasks were allocated to the satellites, and each satellite would schedule the task respectively in single satellites scheduling phase. We adopted an adaptive ant colony optimization algorithm to search the optimal task assignment scheme. Adaptive parameter adjusting strategy and pheromone trail smoothing strategy are introduced to balance the exploration and the exploitation of search process. A heuristic algorithm and a very fast simulated annealing algorithm were proposed to solve the single satellite scheduling problem. The task assignment scheme was valued by integrating the observation scheduling result of multiple satellites. The result was responded to the ant colony algorithm, which can guide the search process of ant colony. Computation results show that the approach is effective to the satellites observation scheduling problem.
出处
《系统工程理论与实践》
EI
CSCD
北大核心
2009年第8期134-143,共10页
Systems Engineering-Theory & Practice
基金
国家自然科学基金(70601035
70801062)
关键词
成像卫星
分解优化
自适应蚁群算法
启发式算法
快速模拟退火
earth observing satellites
decomposition optimization
adaptive ant colony optimization
heuristic algorithm
very fast simulated annealing
