敏捷成像卫星调度的改进量子遗传算法

针对敏捷成像卫星调度问题中解空间大，选择任务的搜索空间和确定任务观测时间的搜索空间分别是离散域和连续域的难题。建立了多种决策变量混合的敏捷成像卫星调度模型，提出一种改进的量子遗传算法对其求解，改进的量子遗传算法采用二进制与实数杂合的编码方式，降低染色体的基因位编码数目，提高了搜索效率，有效适应了敏捷成像卫星调度问题中离散与连续混合的解空间；以杂合编码为基础，设计对应的观测函数将敏捷成像卫星调度问题的解映射到相位空间，从而将量子优化机制引入敏捷成像卫星调度问题中，利用量子遗传算法在相位空间搜索的特性解决敏捷成像卫星解空间大、解空间离散与连续并存的问题。最后，通过不同规模的仿真校验对算法的调度效果进行测试和分析。结果表明，所提改进的量子遗传算法在收敛速度和方案收益方面都有较好的表现，能够满足敏捷成像卫星调度的需要。

Scheduling of Agile Satellites Based on an Improved Quantum Genetic Algorithm

 Aiming at solving the problem of the large solution space and mixed decision variables on agile image satellites scheduling, a scheduling model with multi-decision variables is built, and an improved quantum genetic algorithm is proposed in this paper. The improved algorithm adopts a hybrid coding strategy, which combines the binary with the real number coding. The hybrid coding is more concise compared to the classic coding of the satellite scheduling. Based on the hybrid coding strategy, the observation functions are designed to map the solution space of the agile image satellites to the quantum space so that the agile image satellites scheduling problem could be solved effectively with the quantum optimization mechanism. In the end of this paper, the simulations in different sizes are performed to verify the proposed algorithm. The simulation results show that compared with the classic algorithms, the improved quantum genetic algorithm proposed in this paper has better performance in both quality and time efficiency.