基于GA-SA组合算法的山区复杂环境无人机起降点选址  

Site Selection of Unmanned Aerial Vehicle Take-off and Landing Points in Mountainous Complex Environment Based on GA-SA Combination Algorithm

作　　者：李章萍[1] 贺亚蒙 LI Zhang-ping;HE Ya-meng(School of Transportation Science and Engineering,Civil Aviation University of China,Tianjin 300300,China)

机构地区：[1]中国民航大学交通科学与工程学院,天津300300

出　　处：《科学技术与工程》2024年第2期850-857,共8页Science Technology and Engineering

基　　金：天津市教委科研计划项目人文社科一般项目(2020SK049)。

摘　　要：针对山区复杂环境下的物流链前端无人机货运起降点选址和任务分配进行研究。首先以建设成本最小和运输时间满意度最大为目标,综合考虑无人机自身性能和禁飞空域等因素,构建多约束条件下多目标函数的起降点选址和任务分配模型。采用遗传算法(genetic algorithm, GA)和模拟退火算法(simulated annealing algorithm, SA)的组合算法进行求解,首先通过遗传算法得出较优的可行解,再以此解作为退火算法的初始解进行模型求解。仿真结果表明,构建的多约束模型能够实现预期效果,并且采用的算法解决此类问题时具有良好的适用性。Site selection and task allocation of UAV(unmanned aerial vehicle)freight take-off and landing points at the leading end of the logistics chain in the complex environment of mountainous areas were studied.First of all,aiming at minmum cost of construction and maximum satisfaction of transportation,the performance of UAV and no-fly airspace were considered,then with multiple constraints and objective functions,the model of site selection and task allocation was constructed.The combinatorial algorithms of genetic and simulated annealing algorithm was adopted.First,the initial feasible solution adopted in annealing algorithm was obtained as optimal feasible solution by the genetic algorithm.What is shown is that the expected effect is achieved by the constructed multi-constraint model,and excellent applicability is shown when the algorithm is adopted to such problems.

关 键 词：无人机货运 多约束条件 多目标函数 起降点选址 组合算法 

分 类 号：V279.2[航空宇航科学与技术—飞行器设计]