摘要
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 import resource in air traffic management system. The purpose is to rationally allocate the flight landing runway and landing sequence and reduce flight delays. This paper analyzes the principle of the adaptive genetic algorithm and the particle swarm optimization algorithm, and then improved each of them. Using the improved GA algorithm combine with the im- proved PSO algorithm, establish the multi-runway flights schedule model. Apply the improved GA-PSO algorithm to solve the established model, and then gives the simulation results at last. The results show that, the improved GA-PSO algorithm can reduce the total delay time and accelerate the convergence speed effectively.
出处
《小型微型计算机系统》
CSCD
北大核心
2014年第9期2110-2115,共6页
Journal of Chinese Computer Systems
基金
辽宁省科技项目博士启动基金项目(20111001)资助
中央高校基本科研业务费(N110417004)资助
辽宁省科技攻关项目(2011216027)资助
关键词
改进粒子群遗传算法
多跑道航班调度
最少延误时间
空中交通管理
航班排序
improved GA-PSO algorthm
multi-runway flights landing schedule
the minimum total delay time
air traffic management
flight sequencing
