基于退火遗传算法的小推力轨道优化问题研究

利用退火遗传算法解决小推力轨道优化问题。首先利用传统混合法将轨道优化问题归结为受非线性方程约束的参数优化问题。通过结合退火和随机惩罚函数对约束条件进行处理后，用遗传算法求解这个参数优化问题。最后再采用局部优化算法提高解的精度。这种算法既保持了传统混合法精度高、解轨线光滑的优点，又克服了传统轨道优化方法收敛性差、初始猜测困难、容易陷入局部极小解的缺点。在本文的最后，利用文中提出的轨道优化算法求解“喷-停-喷”型定常推力幅值地球-木星轨道转移问题。算例证明此算法可以有效地求解小推力轨道转移问题，尤其适用于传统轨道优化方法难以求解的复杂轨道优化问题。

Low-Thrust Trajectory Optimization Based on Annealing-Genetic Algorithm

The low-thrust trajectory was optimized by using annealing-genetic algorithm. First, the trajectory optimization problem came down to a nonlinear constraint parameter optimization by using traditional hybrid method. Then, after the constraints being processed by anneal and random penalty functions, the parameter optimization problem was settled by genetic algorithm. Finally, the precision of the final solutions was improved by utilizing localized optimization. This algorithm not only keeps the advantages of hybrid method such as high precision and smooth solutions, but also avoids the defects of traditional methods which are the small radius of convergence, difficulty of initial guesses and local convergence. At the end of this paper, the thrust-coast-thrust Earth-Jupiter orbit transfer with a constant thrust was optimized by utilizing this algorithm, which illustrates the effectiveness of the algorithm in low-thrust transfer orbit optimization. This algorithm especially adapts to complex trajectory optimization problems that can not be evaluated by using traditional methods.