Advertisement

Parallel Max-Min Ant System Using MapReduce

  • Qing Tan
  • Qing He
  • Zhongzhi Shi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7331)

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.

Keywords

Ant colony optimization MMAS Parallel MMAS Travelling salesman problem MapReduce Hadoop 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Stützle, T., Hoos, H.: MAX-MIN ant system. Future Generation Computer System 16(8), 889–914 (2000)CrossRefGoogle Scholar
  2. 2.
    Dorigo, M., Stützle, T.: Ant Colony Optimization. The MIT Press, America (2004)zbMATHCrossRefGoogle Scholar
  3. 3.
    Chu, S.-C., Roddick, J., Pan, J.-S., Su, C.-J.: Parallel Ant Colony Systems. In: Zhong, N., Raś, Z.W., Tsumoto, S., Suzuki, E. (eds.) ISMIS 2003. LNCS (LNAI), vol. 2871, pp. 279–284. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  4. 4.
    Bernd, B., Gabriel, E.K., Christine, S.: Parallel Strategies for the Ant System. University of Vienna, Vienna (1997)Google Scholar
  5. 5.
    Jeffrey, D., Sanjay, G.: MapReduce: Simplified Data Processing on Large Clusters. Communications of the ACM 51, 107–113 (2008)Google Scholar
  6. 6.
    Ralf, L.: Google’s MapReduce Programming Model – Revisited. Science of Computer Programming 70, 1–30 (2008)MathSciNetzbMATHCrossRefGoogle Scholar
  7. 7.
    Grama, A., Gupta, A., Karypis, G., Kumar, V.: Introduction to Parallel Computing, 2nd edn. Addison-Wesley, Harlow (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Qing Tan
    • 1
    • 2
  • Qing He
    • 1
  • Zhongzhi Shi
    • 1
  1. 1.The Key Laboratory of Intelligent Information Processing, Institute of Computing TechnologyChinese Academy of SciencesBeijingChina
  2. 2.Graduate University of Chinese Academy of SciencesBeijingChina

Personalised recommendations