Numerical Simulation Algorithm Study on Dynamic Programming of Training Airspace

Abstract

The dynamic planning of the training airspace is of great significance for improving the utilization rate of the airspace, improving the efficiency of military training, and alleviating the contradiction between military and civilian air. The spatial dynamic programming problem is processed in stages, and the total occupation time is minimized by the optimal scheme of each stage. Aiming at the dynamic programming problem in each stage, on the basis of analyzing the complexity of the problem, the spatial planning model is constructed, and the genetic-discrete particle swarm optimization(DPSO) 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. At the same time, in order to ensure the diversity of population, the adaptive crossover operator and mutation operator are designed to ensure the individual feasibility, and the Gantt chart is used to represent the whole spatial planning process. Finally, the improved genetic-particle swarm optimization algorithm is used as an example. Compared with the traditional particle swarm optimization, the results show that the algorithm is of better results and faster convergence speed.

Keywords

• dynamic programming
• training airspace
• genetic algorithm
• particle swarm optimization algorithm