大型星座混合模拟退火遗传算法测控任务规划  

Massive Constellation Measurement and Control Scheduling Based on Hybrid Simulated Annealing Genetic Algorithm

作　　者：马林 秦阳 秦嘉豪 徐明[3] MA Lin;QIN Yang;QIN Jiahao;XU Ming(School of Astronautics,Space Engineering University,Beijing 101416,China;China Aerospace Science and Technology Corporation,Beijing 100048,China;School of Astronautics,Beihang University,Beijing 102206,China)

机构地区：[1]航天工程大学宇航科学与技术系,北京101416 [2]中国航天科技集团有限公司,北京100048 [3]北京航空航天大学宇航学院,北京102206

出　　处：《宇航学报》2023年第11期1757-1766,共10页Journal of Astronautics

摘　　要：提出了一种基于拥挤度的混合模拟退火多层编码遗传算法,用于解决大型星座的测控任务规划问题。首先,建立了大型星座测控问题的约束满足模型,对任务规划过程中的各种约束条件和优化目标进行了形式化描述。第二,采用多层编码的方法建立测控方案与解空间的对应关系,并采用遗传算法的启发式方法进行全局搜索。第三,为了增加种群的多样性并加快收敛速度,在每代进化结束后对种群中最优的部分个体进行基于拥挤度的模拟退火。超180颗卫星,32个地面测控天线的真实大型星座测控任务规划试验表明,基于拥挤度的混合模拟退火遗传算法的任务完成率高达99%,归一化综合收益大于0.9,算法运行时间小于15 min,验证了该方法具备工程实用价值。A hybrid simulated annealing genetic algorithm(HSAGA)based on congestion is proposed to solve the tracking,telemetry,and control(TT&C)scheduling problem of large constellations.Firstly,the constraints satisfy problem(CSP)model of TT&C scheduling is established by formally describing various constraints and optimization objectives.Secondly,multi-layer encoding method is adopted to map the TT&C schedule into integer sequence,and genetic algorithm is utilized to perform global optimization.Thirdly,in order to enhance population diversity and expedite convergence,at the end of each iteration,the optimal part of the population is processed with congestion-based simulated annealing.The algorithm is applied on real constellation for TT&C.The constellation consists of over 180 satellites and 32 antennas.The application demonstrates that the HSAGA exhibits a task completion rate exceeding 99%,a normalized integrated gain surpassing 0.9,and a time consumption of less than 15 minutes,thereby validating the algorithm’s practical significance.

关 键 词：多星测控 多星任务规划 多层编码遗传算法 基于拥挤度的快速模拟退火 

分 类 号：V529.1[航空宇航科学与技术—人机与环境工程]