训练空域动态规划问题数值模拟仿真算法研究

训练空域的动态规划对于提高空域利用率,提高部队训练效率,缓解军民用空矛盾具有重要意义。本文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来使得总的占用时间最短。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,构建了空域规划模型,提出了遗传-离散粒子群算法,通过融合遗传算法中的交叉与变异思想来改善DPSO算法摆脱局部最优解的能力,提高算法的收敛速度和精度。同时为保证种群的多样性,设计了可保证个体可行性的自适应交叉算子和变异算子。最后利用甘特图来表示整个空域规划过程。将改进后的遗传-粒子群算法用于算例,并与遗传算法比较,结果表明该算法获得的结果更优且收敛速度更快。

Numerical Simulation Study on DynamicProgramming of Training Airspace

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. In this paper, 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 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 gantt chart is used to represent the whole airspace planning process. Compared with the genetic algorithm, the improved gpso is applied to the numerical example