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机场跑道是空中交通管理系统中重要的系统资源.为了合理分配航班的降落跑道和降落顺序,减少航班延误时间,分析了自适应遗传算法和基本粒子群优化算法的运行原理,分别对自适应遗传算法和基本粒子群算法进行改进,将改进自适应遗传算法引进到改进粒子群算法中,建立多跑道航班排序模型,应用改进粒子群遗传算法对跑道调度模型进行求解,并进行算例仿真分析.结果表明,改进混合算法能有效降低总的延误时间并加快收敛速度.
The airport runway is an important system resource in the air traffic management system.In order to rationally allocate the flight landing runway and landing sequence and reduce the flight delay time, the operation principle of the adaptive genetic algorithm and the basic particle swarm optimization algorithm is analyzed, and the adaptive genetic Algorithm and basic particle swarm optimization algorithm. The improved adaptive genetic algorithm is introduced into the improved particle swarm optimization algorithm, and the multi-runway flight scheduling model is established. The improved particle swarm optimization algorithm is used to solve the runway scheduling model, and the simulation analysis is carried out. The results show that the improved hybrid algorithm can effectively reduce the total delay time and speed up the convergence.