面向多星观测调度的分层迭代算法

提出一种基于分治策略的多星观测分层调度框架，在该框架下，用蚁群优化算法把任务分配至各轨道圈次上，并利用自适应模拟退火算法求解各轨道圈次的调度问题。根据各轨道圈次调度结果的反馈情况，再调整任务分配方案，重复上述过程直到达到算法终止条件。为了提高算法的性能，在设计蚁群算法的启发式信息模型时，应充分考虑卫星调度问题的领域知识；在模拟退火算法中设计两个邻域结构，采用动态选择策略在优化过程中确定最佳邻域搜索结构。仿真实验表明，该方法有效地降低了问题求解的复杂度，尤其在求解大规模多星观测调度问题时表现出优异的性能。
A novel satellite scheduling framework based on the divide and conquer principle was proposed. Under this framework, an ant colony optimization algorithm was employed to distribute observation tasks to different satellite orbits. Then, an adaptive simulated annealing algorithm was designed to solve the satellite observation problem involved in each orbit. According to the feedback on the scheduling results at each orbit, the task distribution schema was adjusted. This process was repeated until the termination condition was met. To improve the efficiency of the algorithm, the domain knowledge of the satellite scheduling problem was considered into the heuristic information model of the ant colony optimization algorithm. Next, two neighborhood structures were designed in the simulated annealing algorithm. In addition, the dynamic selection strategy was used to choose the most appropriate neighborhood search structure. Extensive experiments show that the proposed method can reduce the problem complexity effectively, especially in solving the large-scale satellite observation scheduling problems, which exhibits extraordinary performance.