›› 2013, Vol. 26 ›› Issue (2): 146-.

• 论文 • 上一篇    下一篇

基于MapReduce的并行蚁群算法研究与实现

夏卫雷,王立松   

  1. (南京航空航天大学 计算机科学与技术学院,江苏 南京 210016)
  • 出版日期:2013-02-15 发布日期:2013-06-20
  • 作者简介:夏卫雷(1988—),男,硕士研究生。研究方向:云计算环境下的智能交通路径查询算法。E-mail:xiaweilei1988718@163.com。王立松(1969—),男,博士,副教授。研究方向:数据库管理、数据融合。

Research on and Implementation of Parallel Ant Colony Algorithm Based on MapReduce

 XIA Wei-Lei, WANG Li-Song   

  1. (College of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China)
  • Online:2013-02-15 Published:2013-06-20

摘要:

蚁群算法在处理大规模TSP问题耗时较长,为解决这一不足,给出了一种基于MapReduce编程模式的并行蚁群算法。采用MapReduce的并行优化技术对蚁群算法中最耗时的循环迭代和循环赋值部分进行改进,同时运用PC集群环境的优势将具有一定规模的小蚁群分配到对应的PC机上,使其并行执行,减少运行时间。实验证明改进后的并行蚁群算法在大数据集上运行时间明显缩短,执行效率显著提高。

关键词: 蚁群算法, TSP问题, MapReduce, 并行优化

Abstract:

As ant colony algorithm is time consuming in dealing with large-scale TSP problems,a parallel optimization algorithm based on MapReduce programming mode is proposed,which improves the loop and loop assignment part with the most time-consuming by MapReduce parallel optimization technique.Simultaneously,it takes advantage of PC integration environment to assign small ant colony with certain scale to corresponding PC machine and to make it execute in parallel as well as reduce its running time.Experiments show that the operation time of the improved parallel ant colony algorithm dealing with large data sets is significantly reduced and execution efficiency is significantly improved.

Key words: ant colony algorithm;the problem of TSP;MapReduce;parallel optimization

中图分类号: 

  • TP301.6