基于混合蚁群优化的天地一体化调度方法  

Space-ground integrated scheduling based on the hybrid ant colony optimization

作　　者：张天骄[1,2] 李济生 李晶[2,3] 杨宜康[1] 杜卫兵[2,3] 

机构地区：[1]西安交通大学电子与信息工程学院,陕西西安710049 [2]宇航动力学国家重点实验室,陕西西安710043 [3]西安卫星测控中心,陕西西安710043

出　　处：《系统工程与电子技术》2016年第7期1555-1562,共8页Systems Engineering and Electronics

基　　金：国家自然科学基金(11503096);国家高技术研究发展计划(863计划)(2014AA7013035);国家重点基础研究发展计划(973计划)(613237201506);国家重大专项基金(GFZX0301030312);宇航动力学国家重点实验室开放基金(2013GKJ11-ADL)资助课题

摘　　要：天地测控资源一体化调度问题是一个典型的大规模组合优化问题,优化过程极其复杂,采用单一优化机制的传统蚁群算法求解这类问题时,存在求解效率低且求解性能差的缺陷。鉴于此,提出了采用两种不同融合策略的新型遗传-蚁群优化方法(genetic-ant colony optimization hybrid algorithm,GA-ACO)求解问题。该方法利用遗传算法的快速搜索、群体性能等优势生成初始蚁群信息素分布,提高了蚁群算法由于运行初期信息素更新较慢导致的较低求解效率和后期早熟引起的较差求解质量。仿真结果表明,相比于基本蚁群算法和遗传算法,混合蚁群算法的寻优性能更好,求解效率更高,更适合解决天地测控资源一体化调度问题。Space-ground telemetry, track and command （TT＆C） resource integrated scheduling problem is a typical large comhinative optimization problem, and its optimization process is very complicated. Single ant colony optimization （ACO） strategy has disadvantages of low efficiency and poor solution performance. For this reason, the genetic-ACO hybrid algorithm, （GA-ACO） which combines the ACO with genetic algorithm （GA） is proposed to solve this problem. The GA is used to accelerate the low optimization efficiency due to the lack of pheromone in ACO in the early stage and prevent premature convergence. Results indicate that the proposed method performs better than the previously presented methods and is a viable and effective approach, which is suitable to solve the space-ground TT＆C resource integrated scheduling problem.

关 键 词：遥测、跟踪和指挥 天地一体化调度 蚁群优化 遗传算法 混合智能算法 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]