基于混合遗传算法的敏捷卫星任务规划求解

针对新一代对地观测敏捷卫星任务规划问题,首先研究了敏捷卫星的特点,分析了敏捷卫星工作模式及对地观测过程,在此基础上构建了基于多目标的任务规划模型。针对敏捷卫星任务规划问题具有多约束、多冲突、非线性NP-hard特点,本文提出了基于混合遗传求解算法,该算法将免疫遗传算法与蚁群算法相结合,以蚁群算法所产生的解作为免疫遗传算法的初始种群,同时以蚁群算法中的全局最优解作为疫苗。通过实验表明本文提出的算法比遗传算法和免疫遗传算法精度更高,收敛速度更快。

Mission Planning for Agile Earth Observation Satellites Based on Hybrid Genetic Algorithm

For a new generation of earth observation Agile satellite mission planning problems, this paper studied the characteristics of agile earth observation satellites(AEOS), and built multi-objective mission planning model on the analysis of its work mode and the process of observation earth targets. Hybrid genetic algorithm was proposed for mission planning problems of AEOS which were multi-constraint, multi-conflict, non-linear NP-hard problems. This algorithm combined of ant colony algorithm and immune genetic algorithm, which was to take the solutions generated by ant colony algorithm as the initial population of immune genetic algorithm, and global optimal solution of ant colony algorithm as a vaccine. Simulation results show that this algorithm had higher accuracy and faster convergence than the genetic algorithm and immune genetic algorithm.