采用混合遗传算法的敏捷卫星自主观测任务规划

Agile satellite autonomous observation mission planning using hybrid genetic algorithm

摘要:

为改进敏捷卫星观测大规模地面目标点时传统的遗传算法求解效率低下的问题,提高智能优化算法的求解效率,改进了传统的遗传算法,提出了禁忌退火遗传混合算法。首先,考虑到航天器在观测地面目标点的过程中所面临的时间约束、姿态轨道动力学约束等多种约束条件,建立了相应的适应度函数。所提出的适应度函数能够兼顾高观测收益与低观测能耗,反应了实际工程问题的观测需求。随后,为改进传统遗传算法的变异过程,提出了禁忌退火变异方法。这一变异方法在个体变异寻优的过程中,引入了禁忌搜索方法与Metropolis法则,提高了算法搜寻到全局最优解的概率,加快了算法的收敛速度。研究结果表明,与传统的遗传算法相比,禁忌退火遗传混合算法节省了约40%的算法运行时间,该算法的运行效率也高于退火遗传算法、禁忌遗传算法等其他种类改进的遗传算法,从而验证了禁忌退火遗传混合算法求解敏捷观测卫星任务规划问题的高效性。

Abstract:

To improve the efficiency of the traditional genetic algorithm when the agile satellite observes large-scale ground target points and increase the solution efficiency of intelligent optimization algorithms, the traditional genetic algorithm was improved, and a tabu search-simulated annealing genetic hybrid algorithm was proposed. First, considering the time constraints and attitude orbit dynamics constraints of spacecraft in observing ground target points, the corresponding fitness function was established. The proposed fitness function could guarantee high observation gains and low observation energy consumption, and reflect the observation requirements of practical engineering problems. Subsequently, to improve the mutation process of the traditional genetic algorithm, a tabu search-simulated annealing mutation method was proposed. This mutation method introduced the tabu search method and Metropolis rule in the process of individual mutation optimization. As a result, the tabu search-simulated annealing mutation method could improve the probability of obtaining the optimal global solution, and accelerate the convergence speed of the algorithm. Compared with the traditional genetic algorithm, simulation results showed that the tabu search-simulated annealing genetic hybrid algorithm saved about 40% of the running time. The operating efficiency of the algorithm was also higher than that of other improved genetic algorithms such as simulated annealing genetic algorithm and tabu search genetic algorithm. The results verified the efficiency of the tabu search-simulated annealing genetic hybrid algorithm in solving the mission planning problem of agile observation satellite.