摘要
为了扩大机场空侧容量,优化机场场面交通调度信息,以优化抵达航班的滑行路径为目标,通过分析机场场面滑行调度问题,构建了机场场面滑行的数学模型。采用蚁群算法结合该数学模型,并对蚁群算法信息素的更新采取自适应性改进,应用于国内某枢纽机场的局部滑行道系统和停机坪系统,对抵达航班的滑行路径进行优化设计。并用Matlab对两种算法实现计算机仿真。实验结果表明:与普通蚁群算法相比,自适应蚁群算法降低了抵达航班波的滑行时间,加快了迭代收敛速度,并缩短了抵达航班的滑行距离,同时减少了航班等待时间。即自适应蚁群算法实现了抵达航班的滑行路径优化设计。
To enlarge the airside capacity of airport and optimize the scheduling information of airport surface traffic,aiming at optimizing the taxiing-route of arrived flights,through analyzing the taxiing-scheduling problem of the airport surface,a mathematical model of airport surface taxiing was structured in the paper.Then an ant colony algorithm （ACA） was combined with the mathematical model,and the updating of pheromone of ant colony algorithm was improved adaptively,which has been applied to part of the taxiway system and the apron system of a domestic hub airport to optimize the taxiing-route of arrived flights.The computer simulations were processed based on Maflab with two different algorithms.The experimental results demonstrate that the taxiing time of flight-flow is declined,the iteration rate of convergence is accelerated,the distance of the flight is reduced and the waiting delay time of the flight is diminished with adaptive ant colony algorithm （Adaptive ACA） compared with ACA.To sum up,the taxiing-route optimization design of arrived flights is realized with Adaptive ACA.
出处
《计算机仿真》
北大核心
2017年第3期62-66,184,共6页
Computer Simulation
基金
国家自然科学基金委员会-中国民航局民航联合研究基金资助项目(U1533203)
中央高校基金项目(3122014P003)
