基于蚁群优化-模拟退火的天地测控资源联合调度  

Solving Space and Ground TT&C Resources Integrated Scheduling Problem with Ant Colony Optimization-Simulated Annealing 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,哈尔滨150080

出　　处：《宇航学报》2012年第11期1636-1645,共10页Journal of Astronautics

基　　金：中国博士后科学基金(20100471044);深空探测着陆与返回控制技术国防重点学科实验室开放基金(HIT.KLOF.2009071);国家自然科学基金(60803051)

摘　　要：研究了一类约束条件复杂、具有多时间窗口特性的天地测控资源联合调度问题。建立了涵盖多种约束的问题数学模型和基于测控弧段时间序的无圈有向图模型,提出了一种蚁群优化-模拟退火算法(ACO-SA)求解问题。探讨了两种算法的融合策略,完成了包括状态转移规则、可行解生成策略、信息素更新准则、邻域结构、快速退火计划等关键技术的设计实现。多个算例仿真和结果分析表明,测控弧段时间序能保证ACO-SA得到高质量的优化结果,模拟退火机制能有效提高蚁群优化算法的收敛速度和求解质量,相比于基本遗传算法和先到先服务算法,ACO-SA能得到质量更好的解。The space-ground TT＆C resource integrated scheduling problem with complex constraint conditions and multi-time windows is researched. First, a mathematical model and an acyclic directed graph model are established according to the characteristics of TY＆C resources. Second, a hybrid method with ant colony optimization algorithm and simulated annealing algorithm （ACO-SA） is put forward to solve the problem. The hybrid strategy including state transition rule, solution generation strategy, fast annealing scheme, neighborhood structure design and so on is discussed and a series of key techniques are implemented. Finally, though simulation cases and computational results analysis, it is found that the simulated annealing mechanism can accelerate the convergence and improve the solution quality for ACO, and ACO-SA outperforms both genetic algorithm （GA） and first coming first serving （FCFS） algorithm.

关 键 词：测控资源调度 天地一体化 无圈有向图 蚁群优化算法 模拟退火算法 

分 类 号：V525[航空宇航科学与技术—人机与环境工程]