自适应大邻域搜索算法在无人机物流路径规划问题中的应用  

Adaptive Large Neighborhood Search Algorithm in Planning of UAV Logistics Route

作　　者：李晓辉[1] 李沛帆 于振宁 赵毅 LI Xiao-Hui;LI Pei-Fan;YU Zhen-Ning;ZHAO Yi(School of Electronics and Control Engineering,Chang’an University,Xi’an 710064,China;CNPC Bohai Equipment Manufacturing Co.Ltd.,Cangzhou 062658,China)

机构地区：[1]长安大学电子与控制工程学院,西安710064 [2]渤海装备华油钢管有限公司,沧州062658

出　　处：《计算机系统应用》2021年第11期260-265,共6页Computer Systems & Applications

基　　金：工信部国家物联网重点研发项目(2019ZDLGY03-01);陕西省重点产业链项目(201805045YD23CG29)。

摘　　要：近年来无人机在物流运输领域发展十分迅速,这其中一个重要原因是无人机可以应对各种复杂的交通环境如城市的交通拥堵和乡村偏远地区的较差路况.而路径规划则是其在实际应用过程当中的一个重要环节,本文针对于此设计了一种自适应大邻域搜索算法来解决该问题.该算法通过引入自适应的机制来对传统的邻域搜索进行改善,使其能具有找到更好的解的潜力.在一些经典数据集上的仿真实验显示,本文提出的算法具有较强的鲁棒性和稳定性.另外通过该算法和其他元启发式算法的对比实验验证了本算法能够有效地减少使用无人机进行物流配送的费用.In recent years, drones have developed rapidly in the field of logistics and transportation. An important reason is that drones could cope with various complex traffic environments such as urban traffic congestion and poor road conditions in remote rural areas. Path planning is a key part of their practical application process. This study designs an adaptive large neighborhood search algorithm for it. The algorithm improves the traditional neighborhood search by introducing an adaptive mechanism, so that it has the potential to find better solutions. Simulation experiments on some classic datasets show that the proposed algorithm has strong robustness and stability. In addition, comparative experiments with other meta-heuristic algorithms verify that the proposed algorithm can effectively reduce the cost of logistics distribution with a drone.

关 键 词：元启发式算法 无人机 物流配送 路径规划 自适应大邻域搜索算法 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] F252[自动化与计算机技术—控制科学与工程]