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计算机工程 ›› 2011, Vol. 37 ›› Issue (24): 190-192.doi: 10.3969/j.issn.1000-3428.2011.24.063

• 人工智能及识别技术 • 上一篇    下一篇

混合蚁群算法在车辆路径问题中的应用

张 潇，王江晴   

  1. (中南民族大学计算机科学学院，武汉 430074)
  • 收稿日期:2011-03-23 出版日期:2011-12-20 发布日期:2011-12-20
  • 作者简介:张 潇(1986－)，男，硕士研究生，主研方向：蚁群算法，人工智能；王江晴，教授、博士
  • 基金项目:
    国家自然科学基金资助项目“复杂环境下动态车辆路径问题的建模与优化”(60842004)

Application of Hybrid Ant Colony Algorithm in Vehicle Routing Problem

ZHANG Xiao, WANG Jiang-qing   

  1. (School of Computer Science, South-central University for Nationalities, Wuhan 430074, China)
  • Received:2011-03-23 Online:2011-12-20 Published:2011-12-20

PDF

990

被引次数

35 | 2

摘要： 蚁群算法在求解车辆路径问题过程中存在搜索时间长、易于陷入局部最优解的问题。为此，设计并实现一种混合蚁群算法。引入变异算子增强算法的全局搜索能力，采用2-opt法优化阶段最优解的子路径。通过对信息素的挥发因子进行动态调整，从而有效控制信息量的变化速度。实例仿真结果表明，该算法具有较好的求解效率和寻优效果。

关键词: 车辆路径问题, 混合蚁群算法, 变异算子, 线路改进, 动态规划

Abstract: Ant Colony Algorithm(ACA) has some short-comings such as its slow computing speed, and it is easy to fall in a local optimal. Based on the idea of ACA, a hybrid optimization algorithm for solving Vehicle Routing Problem(VRP) is proposed. The algorithm expands the scope of solution space and improves the global ability of the algorithm by importing mutation operator, optimizes the stage optimal solution further by combining 2-opt, and controls the rate of change in pheromone by adjusting configuration of parameters dynamically. Example simulation results show that this algorithm can get optimal resolution of VRP effectively and quickly.

Key words: Vehicle Routing Problem(VRP), hybrid Ant Colony Algorithm(ACA), mutation operator, line improvement, dynamic programming

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