基于混合粒子群的航班着陆调度优化研究

Study on Aircraft Landing Scheduling Optimization Based on Hybrid Particle Swarm Optimization

研究终端区航班着陆调度优化控制问题,为对多目标着陆实现实时调度,克服粒子群算法易陷入局部最优的问题,提出了一种免疫思想和禁忌搜索的混合粒子群调度算法,在粒子群算法的基础上引入了免疫系统的抗体浓度调节机制,以保证群体多样性。针对算法后期进化速度慢的缺点,采用了具有自适应能力的禁忌搜索算法进一步优化性能。最后将混合粒子群调度算法在不同规模的实例上进行了测试,并与其它几种具有代表性的算法进行了比较。实验结果表明,改进算法不仅较好地避免了陷入局部最优,提高了收敛速度,还有效地减少了航班着陆调度中的延迟。

In order to achieve real-time scheduling multi-objective landing scheduling and overcome the defection of the particle swarm algorithm that is easy to fall into local optimum,a hybrid particle swarm optimization algorithm based on the immune idea and tabu search was proposed.The concentration of the antibodies regulatory mechanism was introduced to ensure the diversity of the group.In addition,the tabu search algorithm with self-adaptive capabilities was presented to optimize the performance of the algorithm and to overcome the shortcoming of evolving slowly in the later phase.Finally,the improved algorithm was tested on different scare instances and compared with several other representative algorithms.The experimental results show that the improved algorithm can not only avoid falling into local optimum and improve the convergence rate,but also reduce the delay effectively in the process of scheduling aircraft landing.