面向多星区域观测调度的改进型自适应遗传算法

Aiming at the shortcomings of the traditional optimization algorithm in solving the multi-satellite regional scheduling problem such as slow convergence speed and being prone to fall into the local optimal solution, an improved adaptive genetic algorithm was proposed. The algorithm uses Monte Carlo method combined with Hamming distance to give a better initial population. According to the average Hamming distance of the population, the execution sequence of crossover and mutation operations are determined. The Sigmoid function and Gaussian function are combined to design the adaptive nonlinear crossover rate and mutation rate based on the individual fitness of the population. The dual elite retention strategy and tournament strategy are combined to ensure the inheritance of the optimal individual. Dual shutdown condition is used to improve the search efficiency of the algorithm. Finally, experiment shows that the method can significantly improve the global search ability, accelerate the convergence speed of the algorithm, and effectively improve the observation efficiency of satellites.