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.