This paper presents a new flight trajectory optimisation method, based on genetic algorithms, where the selected optimisation criterion is the minimisation of the total cost. The candidate flight trajectories evaluated in the optimisation process are defined as flight plans with two components: a lateral flight plan (the set of geographic points that define the flight trajectory track segments) and a vertical flight plan (the set of data that define the altitude and speed profiles, as well as the points where the altitude and/or speed changes occur). The lateral components of the candidate flight plans are constructed by selecting a set of adjacent nodes from a routing grid. The routing grid nodes are generated based on the orthodromic route between the flight trajectory’s initial and final points, a selected maximum lateral deviation from the orthodromic route and a selected grid node step size along and across the orthodromic route. Two strategies are investigated to handle invalid flight plans (relative to the aircraft’s flight envelope) and to compute their flight performance parameters. A first strategy is to assign a large penalty total cost to invalid flight profiles. The second strategy is to adjust the invalid flight plan parameters (altitude and/or speed) to the nearest limit of the flight envelope, with priority being given to maintaining the planned altitude. The tests performed in this study show that the second strategy is computationally expensive (requiring more than twice the execution time relative to the first strategy) and yields less optimal solutions. The performance of the optimal profiles identified by the proposed optimisation method, using the two strategies regarding invalid flight profile performance evaluation, were compared with the performance data of a reference flight profile, using identical input data: initial aircraft weight, initial and final aircraft geographic positions, altitudes and speed, cost index, and atmospheric data. The initial and final aircraft geographic positions, and the reference flight profile data, were retrieved from the FlightAware web site. This data corresponds to a real flight performed with the aircraft model used in this study. Tests were performed for six Cost Index values. Given the randomness of the genetic algorithms, the convergence to a global optimal solution is not guaranteed (the solution may be non-optimal or a local optima). For a better evaluation of the performance of the proposed method, ten test runs were performed for each Cost Index value. The total cost reduction for the optimal flight plans obtained using the proposed method, relative to the reference flight plan, was between 0.822% and 3.042% for the cases when the invalid flight profiles were corrected, and between 1.598% and 3.97% for the cases where the invalid profiles were assigned a penalty total cost.

中文翻译：

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使用遗传算法的新飞行轨迹优化方法

本文提出了一种新的基于遗传算法的飞行轨迹优化方法，其中选择的优化标准是总成本的最小化。在优化过程中评估的候选飞行轨迹被定义为具有两个组成部分的飞行计划：横向飞行计划（定义飞行轨迹轨迹段的一组地理点）和垂直飞行计划（定义高度的一组数据）和速度剖面，以及发生高度和/或速度变化的点）。候选飞行计划的横向分量是通过从路由网格中选择一组相邻节点来构建的。路由网格节点是根据飞行轨迹的起点和终点之间的正交路线生成的，与正向路线的选定最大横向偏差以及沿和跨正向路线的选定网格节点步长。研究了两种策略来处理无效的飞行计划（相对于飞机的飞行包线）并计算它们的飞行性能参数。第一个策略是对无效的飞行配置文件分配一个大的惩罚总成本。第二种策略是将无效的飞行计划参数（高度和/或速度）调整到最近的飞行包线限制，优先保持计划的高度。本研究中进行的测试表明，第二种策略的计算成本很高（相对于第一种策略，执行时间是第一种策略的两倍以上），并且产生的最优解也较少。通过所提出的优化方法确定的最佳剖面的性能，使用关于无效飞行剖面性能评估的两种策略，与参考飞行剖面的性能数据进行比较，使用相同的输入数据：初始飞机重量、初始和最终飞机地理位置、高度和速度、成本指数和大气数据。从 FlightAware 网站检索初始和最终飞机地理位置以及参考飞行剖面数据。该数据对应于使用本研究中使用的飞机模型进行的实际飞行。对六个成本指数值进行了测试。鉴于遗传算法的随机性，不能保证收敛到全局最优解（该解可能是非最优解或局部最优解）。为了更好地评估所提出方法的性能，对每个成本指数值进行了十次测试运行。使用所提出的方法获得的最佳飞行计划的总成本降低，相对于参考飞行计划，在纠正无效飞行剖面的情况下，总成本降低在 0.822% 和 3.042% 之间，在纠正情况下在 1.598% 和 3.97% 之间其中无效配置文件被分配了罚款总成本。