基于分治框架的元启发式与精确算法融合方法用于多卫星观测调度
Ensemble of meta-heuristic and exact algorithm based on the divide and conquer framework for multi-satellite observation scheduling
AI总结：
针对大规模多卫星观测调度问题，提出基于分治框架的元启发式与精确算法融合方法EHE-DCF，通过迭代的任务分配与调度阶段求解，相比现有算法能获得更高调度收益、完成更多任务，对大规模问题效率突出。
摘要：
卫星观测调度对于提升对地观测系统的效率具有重要作用。为解决大规模多卫星观测调度问题，本文提出一种基于分治框架的元启发式与精确算法融合方法（EHE-DCF），包含任务分配阶段和任务调度阶段。在任务分配阶段，基于融合了概率选择机制和禁忌机制的元启发式算法，将每项任务分配至合适的轨道，其中概率选择机制和禁忌机制分别源自蚁群优化算法和禁忌搜索算法。在任务调度阶段，我们为每条单独轨道构建任务调度模型，并采用精确方法（即分支定界法，B&B）求解该模型。任务分配与任务调度阶段迭代执行，以得到优质解。为验证EHE-DCF的性能，我们将其与B&B、三种基于分治的元启发式算法以及一种当前最优的元启发式算法进行对比。实验结果表明，与现有算法相比，EHE-DCF能够获得更高的调度收益并完成更多任务。EHE-DCF在处理大规模卫星观测调度问题时效率尤为突出。
英文摘要：
Satellite observation scheduling plays a significant role in improving the efficiency of Earth observation systems. To solve the large-scale multi-satellite observation scheduling problem, this paper proposes an ensemble of meta-heuristic and exact algorithm based on a divide-and-conquer framework (EHE-DCF), including a task allocation phase and a task scheduling phase. In the task allocation phase, each task is allocated to a proper orbit based on a meta-heuristic incorporated with a probabilistic selection and a tabu mechanism derived from ant colony optimization and tabu search respectively. In the task scheduling phase, we construct a task scheduling model for every single orbit, and use an exact method (i.e., branch and bound, B&B) to solve this model. The task allocation and task scheduling phases are performed iteratively to obtain a promising solution. To validate the performance of EHE-DCF, we compare it with B&B, three divide-and-conquer based meta-heuristics, and a state-of-the-art meta-heuristic. Experimental results show that EHE-DCF can obtain higher scheduling profits and complete more tasks compared with existing algorithms. EHE-DCF is especially efficient for large-scale satellite observation scheduling problems.
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