免疫教与学算法在航空器优化排序中的应用

Application of Immune Teaching-Learning-Based Optimization in Aircraft Optimal Sequencing

进场航空器优化排序与调配对于保障飞行安全、降低飞行成本具有重要的意义。建立了以最小延误时间为目标函数的多约束进场航空器优化排序模型,基于"教与学"算法(TLBO),对算法进行离散化,并结合免疫算法(IA)的"抗体注入"进行改进。使用改进后的算法对航空器排序优化模型进行仿真分析,并与传统FCFS方法进行对比。仿真结果表明:与FCFS方法相比,免疫教与学算法使航空器总延误时间有了明显降低,有效缓解了航空器进场延误,可以应用于解决航空器优化排序问题。

Sequencing and scheduling of approaching aircrafts is of great significance for ensuring flight safety and reducing flight costs. A multi-constraint approach aircraft optimization sequencing model with minimum delay time as an objective function was established. Based on the "Teaching-Learning Based Optimization"(TLBO),the algorithm was discretized,combined with the "antibody injection"of the Immune Algorithm(IA). The improved algorithm was used to simulate the aircraft sequencing optimization model and compared with the traditional FCFS method. The simulation results show that compared with the FCFS method,the improved TLBO algorithm can significantly reduce the total aircraft delay,effectively mitigate the approaching delay of aircraft,and can be used to solve the problem of aircraft optimization.