摘要
采用一种改进的人工蜂群算法(IABC)求解标准车辆路径问题.针对基本人工蜂群算法易陷入局部极小、收敛较慢等缺陷,提出了6种邻域生成策略,并基于此设计了新的局部搜索算法.引领蜂和跟随蜂根据该算法在邻域空间内更新当前解.通过小规模和大规模算例的仿真实验,将本文算法与其它智能算法以及基本人工蜂群算法进行了比较,验证了本文提出的算法无论在有效性还是稳定性上都具有良好的效果.
In this paper, we use an improved artificial bee colony algorithm（IABC）for solving standard vehicle routing problem. In order to avoid slow convergence and local optimum, we propose six neighborhood generating strategies. Meanwhile, we design a new local search algorithm based on the strategies. The leader bees and follower bees update the current solution in the neighborhood by this algorithm. Through the simulation experiment of large and small scale examples, the proposed algorithm was compared with some intelligent optimization algorithms and the basic artificial bee colony algorithm. Experimental results show that the proposed algorithm has good performance in both effectiveness and stability.
出处
《湖北文理学院学报》
2016年第2期9-14,共6页
Journal of Hubei University of Arts and Science
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)
安徽省高校优秀青年人才基金重点项目(2013SQRL111ZD)
关键词
车辆路径问题
离散蜂群算法
邻域生成策略
局部搜索算法
Vehicle routing problem
Improved artificial bee colony algorithm
Neighborhood generating strategy
Local search algorithm
