基于蚁群算法的空间目标地基监视重调度

为解决空间目标地基监视中的资源故障或因天气、供电导致的资源失效等对调度的干扰问题,在扰动度量的基础上提出一种基于扰动邻域搜索的蚁群算法.建立以失败需求综合优先级最小和扰动最小为目标的重调度模型,算法中采用2个信息素矩阵来产生一个可行解,通过邻域搜索在可行解的扰动邻域内对其进行局部优化,再用蚁群算法进行全局寻优.仿真实验结果表明:该算法是有效、可行的,能在可接受时间内收敛,且其解的质量相对启发式方法有明显提升.

Ground-based Space Surveillance Rescheduling Based on Ant Colony Optimization

In order to solve the disruption problem caused by resource fault or resource failure due to weather or power supply in scheduling for ground-based space surveillance, a rescheduling model was constructed with the optimal objective which was a weighted sum of disturbances as well as integrated priority of failed demands. Through the disturbances metric in the rescheduling process, a rescheduling algorithm based on disturbance neighborhood searching and ant colony algorithm was proposed. Through the algorithm, two pheromone matrices were used to generate a feasible solution; local optimization was adopted to improve the feasible solution in its neighborhood; then ant colony algorithm was adopted for global optimization. Experimental results from simulation of resource failure showed that the proposed algorithm was useful and feasible.