[1]朱晓东,王鼎.求解双目标VRPTW的改进混合蚁群算法[J].郑州大学学报(工学版),2020,41(04):52-58.[doi:10.13705/j.issn.1671-6833.2020.04.004]
　Zhu Xiaodong,Wang Ding.An Improved Hybrid Ant Colony Algorithm for Bi-objective VRPTW windows[J].Journal of Zhengzhou University (Engineering Science),2020,41(04):52-58.[doi:10.13705/j.issn.1671-6833.2020.04.004]
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求解双目标VRPTW的改进混合蚁群算法()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
41卷
期数:
2020年04期
页码:
52-58
栏目:
出版日期:
2020-08-12

文章信息/Info

Title:
An Improved Hybrid Ant Colony Algorithm for Bi-objective VRPTW windows
作者:
朱晓东; 王鼎
郑州大学电气工程学院
Author(s):
Zhu Xiaodong; Wang Ding
School of Electrical Engineering, Zhengzhou University
关键词:
蚁群算法; 车辆路径问题; 时间窗; 双目标
Keywords:
ant colony algorithm' target="_blank" rel="external">">ant colony algorithm; vehicle routing issues; time windows; Dual target; 
DOI:
10.13705/j.issn.1671-6833.2020.04.004
文献标志码:
A
摘要:
为了解决基本混合蚁群算法在求解大规模带时间窗车辆路径问题（VRPTW）时存在的问题，提出一种改进的双目标混合蚁群算法。首先在节点选择上使用周边选择策略提升选择效率，并提出一种首节点选择策略来加速算法收敛；其次在信息素叠加公式上增加了和车辆数有关的惩罚函数，使算法在优化距离的同时优化车辆数；最后提出一种新的局部优化算，通过将节点数较少的线路中的节点插入到其他线路来提升车辆利用率。算法在Solomon标准数据集上的实验和对比，说明了改进的算法具有搜索能力强，收敛速度快，鲁棒性强等优点。
Abstract:
In order to solve the problems of basic hybrid ant colony algorithm in solving large-scale VRPTW, an improved bi-objective hybrid ant colony algorithm is proposed. Firstly, the peripheral selection strategy is used to improve the selection efficiency, and a first node selection strategy is proposed to accelerate the convergence of the algorithm. Secondly, a penalty function related to the number of vehicles is added to the pheromone superposition formula to optimize the number of vehicles while optimizing the distance. Finally, a new local optimization algorithm is proposed to improve vehicle utilization and expand the neighborhood solution by inserting nodes in routes with fewer nodes into other routes. Experiments and comparisons on Solomon benchmark problems show that the improved algorithm has the advantages of strong search ability, fast convergence speed and strong robustness.

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更新日期/Last Update: 2020-10-06