训练空域动态规划问题数值模拟仿真算法研究

Numerical Simulation Algorithm Study on Dynamic Programming of Training Airspace

对训练空域进行动态规划,有利于提高空域利用率和部队训练效率。将空域的动态规划问题进行分阶段处理,通过寻求各个阶段的最优方案使得总占用时间最短。针对各个阶段的动态规划问题,在分析问题复杂性的基础上,构建空域规划模型,提出遗传-离散粒子群优化算法,通过融合遗传算法中的交叉与变异思想来提高离散粒子群优化(DPSO)算法摆脱局部最优解的能力,从而提高算法的收敛速度和精度;同时为了保证种群的多样性,设计可保证个体可行性的自适应交叉算子和变异算子;利用甘特图来表示整个空域的规划过程。将提出的遗传-粒子群优化算法用于算例,并与传统粒子群优化算法进行比较,结果表明:该算法获得的结果更优且收敛速度更快。

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.