Research on Dynamic Programming of Training Airspace Based on Genetic-Particle Swarm Optimization Algorithm

摘要

Aiming at the dynamic programming problem of training airspace, on the basis of analyzing the complexity of the problem, the spatial planning model is constructed, and the genetic-discrete particle swarm optimization algorithm is proposed. By integrating the crossover and mutation ideas in the genetic algorithm, the DPSO algorithm's ability to get rid of the local optimal solution is improved, and the convergence speed and accuracy of the algorithm are improved. In order to ensure the diversity of the population, the adaptive crossover operator and mutation operator are designed. Finally, The improved genetic-particle swarm optimization algorithm is used as an example. Compared with the traditional PSO algorithm, the results show that the algorithm has better results and faster convergence speed.