Multi-satellites Mission Scheduling is an optimization problem with multi-tasks conflicts and multi-resources constrains, it becomes more and more important as the number of satellites increases rapidly. In view of the problem, the feedback of ACO and the local search ability of SA are combined, then an improved simulated annealing algorithm is designed. A specific description on the knowledge definition, knowledge update rules and tasks conflicts managements is given lately. Results from simulation show that the proposed algorithm performs better than GA (Genetic Algorithm) and ACO ( Ant Colony Algorithm) , they also testify the validity of the improved simulated annealing algorithm.%多星任务规划是一个多任务冲突、多资源约束的优化问题.随着卫星数量的日益增多,其地位越来越重要.针对该问题,综合蚁群算法的反馈特性和模拟退火算法的局部搜索特性,设计了一种基于知识的改进模拟退火算法.并对知识定义、知识更新规则和任务冲突处理策略做了详细描述.仿真表明算法在性能上比遗传算法(Genetic Algorithm,GA)和蚁群算法(Ant Colony Algorithm,ACO)均有一定的优势,证明了改进模拟退火算法的有效性.
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