改进遗传算法与多目标优化模型的航班路径规划

Flight path planning based on improved genetic algorithm and multi-objective optimization model

针对现有航线路径规划模型,单一成本规划难以解决不同机型及运输时间条件下最优路径规划的问题,将机型配置、运输时间和系统成本相结合,通过枢纽城市位置、非枢纽城市节点向枢纽城市节点的流量分配、机队飞行时间以及机队规模建立枢纽航线网络优化模型。以飞行时间与系统总成本为最小化求解目标,利用熵值法建立染色体选择机制,引入自适应交叉率改进遗传算法,通过改进算法(IGA)对最佳航线及枢纽节点位置分布优化求解,并与传统遗传算法、人工蜂群算法和灰狼算法进行对比。研究表明,将不同机型配置、运输时间进行组合,优于单一成本路径规划。以改进算法对枢纽航线网络模型进行优化求解,系统总成本降低了3.41×10^(10),为机队资源的合理配置提供了借鉴。

For the existing path planning models,it is difficult to solve the problem of optimal path planning under different aircraft types and transportation time conditions with a single cost planning.This paper combines aircraft type configuration,transportation time and system cost,and establishes an optimization model of hub-and-spoke airline network through the location of hub cities,the flow distribution from non-hub city nodes to hub city nodes,the flight time of the fleet and the fleet size.The entropy method is used to establish the chromosome selection mechanism,and the adaptive crossover rate is introduced to improve the genetic algorithm.The improved algorithm(IGA)is used to optimize the location distribution of the best route and hub nodes.Our method is compared with the traditional genetic algorithm,bee colony algorithm and grey Wolf algorithm.The research indicates that combining different aircraft type configurations and transportation times is superior to single-cost path planning.By optimizing and solving the hub-and-spoke airline network model with the improved algorithm,the total system cost is reduced by 3.41×10^(10),providing a reference for the reasonable allocation of fleet resources.