Parallel max-min ant system using mapreduce

  • Authors:

  • Qing Tan;Qing He;Zhongzhi Shi

  • Affiliations:

  • The Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China,Graduate University of Chinese Academy of Sciences, Beijing ...;The Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;The Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

  • Venue:
  • ICSI'12 Proceedings of the Third international conference on Advances in Swarm Intelligence - Volume Part I
  • Year:
  • 2012

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Abstract

Ant colony optimization algorithms have been successfully applied to solve many problems. However, in some large scale optimization problems involving large amounts of data, the optimization process may take hours or even days to get an excellent solution. Developing parallel optimization algorithms is a common way to tackle with this issue. In this paper, we present a MapReduce Max-Min Ant System (MRMMAS), a MMAS implementation based on the MapReduce parallel programming model. We describe MapReduce and show how MMAS can be naturally adapted and expressed in this model, without explicitly addressing any of the details of parallelization. We present benchmark travelling salesman problems for evaluating MRMMAS. The experimental results demonstrate that the proposed algorithm can scale well and outperform the traditional MMAS with similar running times.