摘要： 为解决机场在交通高峰期的航班着陆动态调度问题，提出一种结合免疫思想的离散粒子群优化算法。将免疫系统多样性保持能力和粒子群优化算法明确方向性搜索的优势相结合，避免在待调度航班队列更新时，由于动态调用排序算法很难获得稳定排序结果而造成的额外开销。实验结果表明，该算法具备高效的全局搜索能力，能在一个雷达扫描周期内，为管制员提供一个稳定的调度方案。

关键词: 航班着陆调度, 粒子群优化算法, 调整序, 免疫记忆, 疫苗接种, 免疫选择

Abstract: In order to solve the problem of dynamic flight landing schedule in the busiest airport, a Discrete Particle Swarm Optimization(DPSO) algorithm combined with immune thinking is used. The algorithm has the advantages of ability to maintain diversity of the immune system and specific directional search of particle swarm algorithm. It avoids effectively the problem that it is hard to get stable schedule result and bring the additional cost when flight queue to be scheduled is updated, and have a certain real-time capability. Experimental results show that the algorithm has efficient global search capability, and can supply controller with a stable schedule scheme in a radar scan cycle.

Key words: flight landing schedule, Particle Swarm Optimization(PSO) algorithm, adjustment sequence, immune memory, vaccination, immune selection

中图分类号: 

• TP391.9