Training Airspace Dynamic Programming in Stages Using Improved Particle Swarm Optimization
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Haiqing Huang, Yarong Wu, Xu Yao, Shuangyan Chen
Dynamic planning of training airspace is of great significance for improving airspace utilization, improving troop training efficiency, and alleviating the conflicts between military and civilian air use. In this paper, the dynamic planning problem in the airspace is processed in stages, and the improved genetic-particle swarm algorithm is used in the calculation examples, and the total occupancy time is minimized by seeking the optimal solution at each stage, and the feasibility and effectiveness are verified through simulation.
Training Airspace, Dynamic Programming, Particle Swarm Optimization, Genetic Algorithm