Solving the Dynamic Programming Problem for Training Airspace Based on Modified PSO algorithm
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Yanming Wei, Xusheng Gan, Rui Yang, Jingjuan Sun
The dynamic programming model is established for training airspace, and a modified Particle Swarm Optimization (PSO) algorithm is used to solve the dynamic programming problem. Through introducing the crossover and mutation ideas in genetic algorithm, PSO algorithm ability to jump out of local optimal solution is improved with good convergence and accuracy. Simulation shows that the modified PSO algorithm can get a satisfactory application effects in dynamic programming problem for training airspace.
Training Airspace, Dynamic Programming, Particle Swarm Optimization, Genetic Algorithm, Gantt Chart