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计算机系统应用英文版:2022,31(4):268-272
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基于改进蚁群算法的钻井救援车辆路径规划
(1.西安石油大学 计算机学院, 西安 710065;2.山脉科技股份有限公司, 西安 710000)
Path Planning of Drilling Rescue Vehicle Based on Improved Ant Colony Algorithm
(1.Institute of Computer, Xi’an Shiyou University, Xi’an 710065, China;2.Mountain Range Technology Co. Ltd., Xi’an 710075, China)
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Received:June 25, 2021    Revised:July 29, 2021
中文摘要: 为了解决救援车辆路途时间过长导致钻井事故应急救援不及时的问题, 提出一种基于改进蚁群算法的钻井救援车辆路径规划方法. 首先针对基本蚁群算法易陷入局部最优, 且在求解转移概率时仅依据信息素含量和路径长度, 未考虑实际路网中影响道路通行的外界因素等不足, 通过引入路径权重因子和改进路径选择策略, 对基本蚁群算法进行了改进; 然后利用改进的蚁群算法, 以用时最少为目标建立了救援车辆路径规划模型; 最后进行了救援车路径规划仿真实验和实际应用测试, 结果表明本文提出的方法可以合理规划出一条全局最优的救援路径, 能有效地解决钻井救援车辆路径规划问题.
Abstract:To solve the problem of the belated emergency rescue against drilling accidents caused by the long journey time of rescue vehicles, this paper proposes an improved ant colony algorithm for the path planning of drilling rescue vehicles. Firstly, in view of the deficiencies that the basic ant colony algorithm tends to fall into the local optimum, and the transition probability is calculated only depending on pheromone content and path length without the consideration of external factors affecting road traffic in the actual road network, the paper improves the basic ant colony algorithm by introducing path weight factors and optimizing path selection strategies. Then, the improved ant colony algorithm is employed to establish a path planning model for rescue vehicles with the least time as the goal. Finally, simulation experiments and practical application tests are carried out on the path planning of rescue vehicles. The results of experiments and tests show that the proposed method can reasonably plan a global optimal rescue path, which thus effectively solves the path planning problem of drilling rescue vehicles.
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基金项目:陕西省自然科学基础研究计划(2019JM-383)
引用文本:
徐英卓,李凯,周俊.基于改进蚁群算法的钻井救援车辆路径规划.计算机系统应用,2022,31(4):268-272
XU Ying-Zhuo,LI Kai,ZHOU Jun.Path Planning of Drilling Rescue Vehicle Based on Improved Ant Colony Algorithm.COMPUTER SYSTEMS APPLICATIONS,2022,31(4):268-272