基于均衡更新蚁群算法的飞机排序调度

飞机排序调度问题是空中交通管制的一个关键问题，本文在给出飞机排序调度模型的基础上，提出一种均衡更新蚁群算法，利用当前解与全局最优解的差异来均衡地更新信息素，增强算法的全局搜索能力，从而生成更优解。实验结果表明，均衡更新蚁群算法求解飞机排序调度问题时，能用较短时间求出优于对比算法的结果，其性能可以提高12.9%，有助于空中交通管制人员根据实时情况安排合适的飞机着陆顺序。

A Balanced Update Ant Colony Optimization for Aircraft Arrival Sequencing and Scheduling

Aircraft arrival sequencing and scheduling (ASS) is a key problem of air traffic control (ATC). According to the ASS model, a balanced update ant colony algorithm (BUACO) is proposed in this paper. BUACO balanced update the pheromone and enhance the global search ability of the algorithm by taking advantage of the difference between the current solution and the global optimal solution, in order to generate a better solution. The experiments show that BUACO’s performance can be increased by 12.9% with a shorter computation time than the comparison algorithms when solving ASS problem, which is conductive to arrange a suitable flight landing sequence based on real time situation for ATC.