计算机科学 ›› 2014, Vol. 41 ›› Issue (7): 261-265.doi: 10.11896/j.issn.1002-137X.2014.07.054

• 人工智能 • 上一篇    下一篇

基于MapReduce的蚁群优化算法实现方法

王诏远,李天瑞,易修文   

  1. 西南交通大学信息科学与技术学院 成都610031四川省云计算与智能技术高校重点实验室 成都610031;西南交通大学信息科学与技术学院 成都610031四川省云计算与智能技术高校重点实验室 成都610031;西南交通大学信息科学与技术学院 成都610031四川省云计算与智能技术高校重点实验室 成都610031
  • 出版日期:2018-11-14 发布日期:2018-11-14
  • 基金资助:
    本文受国家自然基金项目:基于粒计算的动态更新知识理论与高效算法研究(61175047)资助

Approach for Development of Ant Colony Optimization Based on MapReduce

WANG Zhao-yuan,LI Tian-rui and YI Xiu-wen   

  • Online:2018-11-14 Published:2018-11-14

摘要: 探讨了蚁群算法的几种并行方式与适用场景以及结合云计算编程框架MapReduce的可行性,并将局部搜索类蚁群优化算法抽象为几个组件,分别与MapReduce框架的几个接口对应实现,从而为该类蚁群优化算法在MapReduce框架下实现并行化提供了一种灵活、扩展性好的解决方案。最后通过旅行商问题的仿真实验验证了所提方法的有效性。

关键词: 蚁群优化算法,MapReduce,Hadoop,旅行商问题 中图法分类号TP311文献标识码A

Abstract: This paper discussed several parallel ways of ant colony optimization and their application scenarios as well as the feasibility of combination with the cloud computing framework MapReduce.Ant colony optimization with the local search feature was abstracted into several components,and then several interfaces based on MapReduce were built to implement each component.It provides a flexible,scalable solution for ant colony optimization with the local search feature to implement parallelism with the MapReduce framework.Finally,simulation results validate the proposed approach on the traveling salesman problem.

Key words: Ant colony optimization,MapReduce,Hadoop,Traveling salesman problem

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