基于IP-ACO算法的航天器测控资源调度技术  

Spacecraft TT&C resource scheduling based on improved Pareto ant colony optimization algorithm

作　　者：王海波[1] 徐敏强[1] 王日新[1] 李玉庆[1] 

机构地区：[1]哈尔滨工业大学深空探测基础研究中心,黑龙江哈尔滨150080

出　　处：《系统工程与电子技术》2012年第4期719-725,共7页Systems Engineering and Electronics

基　　金：国家自然科学基金(60803051);国家高技术研究发展计划(863计划)(2005AA735080)资助课题

摘　　要：采用多目标蚁群优化算法对航天器测控资源调度问题进行研究。在分析中低轨道航天器测控特点的基础上,综合考虑包括测控时间窗口约束和设备切换时间约束在内的多类复杂约束条件,建立多目标航天器测控资源调度模型。在Pareto蚁群优化算法的基础上,引入蚁群社会中的分工协作思想并构建测控任务时间约束有向图,设计基于任务选择期望的状态转移规则和基于自适应网格技术的权重更新策略,从而提高算法求解性能。仿真实验结果表明该方法能有效解决多目标航天器测控资源调度问题。Multiobjective ant colony optimization（ACO） algorithm is used to solve the spacecraft tracking teremetry and command（TTC） resource scheduling problem（STRSP）.Based on the analysis of TTC characteristics for low earth orbit and medium earth orbit spacecrafts,a multiobjective mathematical formulation for the STRSP is presented,which takes the time window constraints and setup time constraints into account.Then,an improved Pareto-ACO（P-ACO） algorithm referred to the division of labor and cooperation mechanism is put forward to solve the problem.The problem is formulated as path search of task temporal constraint directed graph and the P-ACO algorithm is improved by designing the state transition rules based on the expectation of task choice and the strategy for weights update based on adaptive grid technique.The experimental results demonstrate the proposed algotithm is effective in solving the multiobjective STRSP.

关 键 词：多目标蚁群优化算法 任务调度 时间约束有向图 自适应网格 

分 类 号：V474[航空宇航科学与技术—飞行器设计]