中文摘要：

为高效解决飞机着陆调度问题,对其离散解空间进行连续化编码,提出经验粒子群（experiential particle swarm optimization,EPSO）算法。提炼飞机着陆调度问题中的领域知识作为每个粒子的经验,优化粒子群算法的寻优过程,增加算法的稳定性。引入控制工程领域中的滚动时域控制（receding horizon control,RHC）策略,在尽量考虑问题完整性的前提下,最大限度降低求解的复杂度,形成最终的RHC-EPSO算法。实验结果表明,该算法能够比现有算法更加高效和稳定地找到飞机着陆调度问题的最优解。

英文摘要：

To solve the aircraft landing scheduling problem efficiently,its discrete solution domain was compiled into a continuous form,and experiential particle swarm optimization（EPSO）algorithm was proposed.The particle swarm optimization＇s process was optimized by the way of extracting the aircraft landing scheduling problem＇s domain knowledge as each particle＇s experience,which increased the stability of the algorithm.The receding horizon control（RHC）belonged to control engineering was also used in the algorithm,which formed the final RHC-EPSO algorithm.Thus,the algorithm＇s complexity was reduced on the premise of solving the problem with integrity.The experiments verify that the algorithm can find the optimal solution of aircraft landing scheduling more efficient and stable than existing algorithms.