摘要
为求解带容量约束车辆路径问题,提出了一种差分算法改进的人工蜂群算法(DABC).针对人工蜂群算法开发能力较弱的缺陷,采用了全局最优解引导的邻域搜索策略.为避免早熟,引入差分算法的交叉更新策略进行局域优化.仿真实验结果证明,混合差分蜂群算法在求解带容量约束车辆路径问题时,能较好地平衡了探索能力和开发能力,在求解速度和稳定性上有良好的效果.
An improved artificial bee colony algorithm(DABC)was proposed to solve the capacitated vehicle routing problem(CVRP).In order to overcome the weakness of the artificial bee colony algorithm,the global optimal solution was used to guide theneighborhood search strategy.The crossover strategy of differential algorithm was introduced to local optimization in order to avoidpremature convergence.The simulation results show that:when solving vehicle routing problems with capacity constraints,the mixeddifferential artificial bee colony algorithm has a better balance between exploration and exploitation abilities,and a good effect on solving speed and stability.
作者
姜婷
JIANG Ting(Department of Information Engineering, Anhui Economic Management College, Hefei, Anhui 230059, China;School of Management, Hefei University of Technology, Hefei, Anhui 230009, China)
出处
《宜宾学院学报》
2017年第12期52-56,共5页
Journal of Yibin University
基金
安徽省哲学社科规划项目(AHSKY2015D71)
安徽省社科创新发展研究课题(A2015020)
关键词
带容量约束车辆路径问题
人工蜂群算法
差分优化算法
邻域搜索策略
交叉更新策略
capacitated vehicle routing problem
artificial bee colony algorithm
differential evolution algorithms
neighborhood search strategy
cross renewal strategy
