基于精英存档自适应微分进化算法的多跑道独立进近排序

随着民航运输业的快速发展，运输需求与空域资源容量之间的矛盾日益突出，导致航班延误的比例也在逐年升高。进港航班排序作为空中交通流量管理的主要手段，能够有效地减少航班延误，减少经济损失，并提高跑道利用率。本文针对进港航班排序问题，建立了一种基于最小化总延误时间的多跑道进港航班排序数学模型，并通过采用精英存档策略和控制参数自适应策略，提出了一种精英存档自适应微分进化算法(EASaDE: Self-adaptive Differential Evolution algorithm with Elite Archive)。在EASaDE中，精英存档策略将当前种群划分为精英种群和非精英种群，参与变异的个体部分来自精英种群，剩余的来自非精英种群；而控制参数自适应策略则将控制参数应用到种群中的每个个体，并根据个体的进化停滞代数来自适应调整参数值。为检验EASaDE的优化性能，本文选取9个常用于优化算法对比的Benchmark测试函数和双跑道进港航班排序实际问题进行实验。从Benchmark函数的优化结果可以看出：EASaDE的优化性能要好于基本DE算法和其它参与对比的改进DE算法。同时，从双跑道进港航班排序的优化结果可以看出：与其它优化算法相比，EASaDE所求得的总延误时间明显更小，规划后的进港序列更为合理。因此，本文提出的EASaDE算法具有较高的收敛精度、收敛速度和稳定性，从而能够有效地减少进港航班队列的总延误时间，提高跑道吞吐量，并减轻管制员的调度压力。

Multi-runways Independent Approach Scheduling Using Self-adaptive Differential Evolution Algorithm with Elite Archive

As the rapid development of civil aviation transportation business, the contradiction between transportation demand and airspace resource capacity has become increasingly prominent, and the proportion of flight delay is increasing year by year. As the main means of air traffic flow management, arrival flights scheduling can effectively decrease flights delay and economic losses, and improve the utilization of the runway. To solve arrival flights scheduling, a mathematical model of multi-runways arrival flights scheduling based on minimizing total delay time was constructed, and a Self-adaptive Differential Evolution algorithm with Elite Archive (EASaDE) was proposed by adopting elite archive strategy and control parameters adaptation strategy. In EASaDE, the elite archive strategy is used to divide the current population into elite population and non-elite population, and the elite archive strategy is used to divide the current population into elite population and non-elite population, and some individuals involving mutation operation are from the elite population and the remainders are from the non-elite population. The control parameters adaptation strategy utilizes the number of individual evolution stagnation to adaptively adjust the control parameters values, which are applied at the individual level. To evaluate the optimization performance of the proposed EASaDE, a total of 9 Benchmark test functions commonly used and dual-runways arrival flights scheduling were used to carry out comparison experiments. From the optimization results of Benchmark test functions, the optimization performance of EASaDE is better than the baisc DE algorithm and the other improved DE algorithms. In addition, from the optimization results of dual-runways arrival flights scheduling, the total delay time obtained by EASaDE is more smaller and the scheduled arrival sequence is more reasonable when compared with the other optimization algorithms. Therefore, the proposed EASaDE has higher convergence precision, faster convergence speed and better robustness, and thus can effectively decrease the total delay time of arrival flights sequence, improve the throughput of the runways, and relieve the scheduling pressure of the controllers.