Advertisement

An Improved Parallel Ant Colony Optimization Based on Message Passing Interface

  • Jie Xiong
  • Xiaohong Meng
  • Caiyun Liu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6145)

Abstract

Ant Colony Optimization (ACO) is recently proposed metaheuristic approach for solving hard combinatorial optimization problems. Parallel implementation of ACO can reduce the computational time obviously. An improved parallel ACO algorithm is proposed in this paper, which use dynamic transition probability to enlarge the search space by stimulating ants choosing new path at early stage; use polymorphic ant colony to improve convergence speed by local search and global search; use partially asynchronous parallel implementation, interactive multi-colony parallel and new information exchange strategy to improve the parallel efficiency. We implement the algorithm on the Dawn 4000L parallel computer using MPI and C language. The Numerical result indicates the algorithm proposed in this paper can improve convergence speed effectively with the fine solution quality.

Keywords

Parallel Ant colony optimization Dynamic transition probability Polymorphic ant colony 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Dorigo, M., Stützle, T.: Ant Colony Optimization. MIT Press, Cambridge (2004)zbMATHGoogle Scholar
  2. 2.
    Dorigo, M., Gambardella, L.M.: Ant colonies for the travelling salesman problem. BioSystems 43, 73–81 (1997)CrossRefGoogle Scholar
  3. 3.
    Gambardella, L.M., Taillard, E.D., Dorigo, M.: Ant colonies for the quadratic assignment problem. Journal of the Operational Research Society 50, 167–176 (1999)zbMATHGoogle Scholar
  4. 4.
    Korosec, P., Silc, J., Robi, B.: Solving the mesh-partitioning problem with an ant-colony algorithm. Parallel Computing 30, 785–801 (2004)CrossRefGoogle Scholar
  5. 5.
    Sun, Z.-x., Xia, Y.-a.: Research on QoS muticast routing algorithm based mixed AntNet algorithm. Journal of Communications 30, 6 (2009)Google Scholar
  6. 6.
    Bullnheimer, B., Kotsis, G., Strauss, C.: Parallelization strategies for the Ant System. Technical Report POM 9-97. Vienna University of Economics and Business Administration (1998)Google Scholar
  7. 7.
    Talbi, E.G., Roux, O., Fonlupt, C., Robillard, D.: Parallel Ant Colonies for the quadratic assignment problem. Future Generation Computer Systems 17, 441–449 (2001)zbMATHCrossRefGoogle Scholar
  8. 8.
    Piriyakumar, D.A.L., Levi, P.: A new approach to exploiting parallelism in ant colony optimization, pp. 237–243 (2002)Google Scholar
  9. 9.
    Randall, M., Lewis, A.: A Parallel Implementation of Ant Colony Optimization. Journal of Parallel and Distributed Computing 62, 1421–1432 (2004)CrossRefGoogle Scholar
  10. 10.
    Blum, C., Roli, A., Dorigo, M.: HC–ACO: The hyper-cube framework for Ant Colony Optimization. In: Proceedings of MIC 2001–Meta–heuristics International Conference, Porto, Portugal, vol. 2, pp. 399–403 (2001); Also available as technical report TR. IRIDIA/2001-16, IRIDIA, Universite Libre de Bruxelles, Brussels, Belgium (2004)Google Scholar
  11. 11.
    Merkle, D., Middendorf, M.: Fast Ant Colony Optimization on Runtime Reconfigurable Processor Arrays. Genetic Programming and Evolvable Machines 3, 345–361 (2004)CrossRefGoogle Scholar
  12. 12.
    Xu, J.-m., Cao, X.-b., Wang, X.-f.: Polymorphic Ant Colony Algorithm. Journal of University of Science and Technology of China 35, 7 (2005)MathSciNetGoogle Scholar
  13. 13.
    Zheng, S., Hou, D.-b., Zhou, Z.-k.: Ant colony algorithm with dynamic transition probability. Control and Decision 23, 4 (2008)Google Scholar
  14. 14.
    Xiong, J., Liu, C., Chen, Z.: A New Parallel Ant Colony Optimization Algorithm Based On Message Passing Interface (2008)Google Scholar
  15. 15.
    Manfrin, M., Birattari, M., Stützle, T., Dorigo, M.: Parallel Ant Colony Optimization for the Traveling Salesman Problem. In: Dorigo, M., Gambardella, L.M., Birattari, M., Martinoli, A., Poli, R., Stützle, T. (eds.) ANTS 2006. LNCS, vol. 4150, pp. 224–234. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  16. 16.

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Jie Xiong
    • 1
  • Xiaohong Meng
    • 1
  • Caiyun Liu
    • 2
  1. 1.Key Laboratory of Geo-detection (China University of Geosciences, Beijing), Ministry of EducationBeijingChina
  2. 2.Freshman Education Department of Yangtze UniversityJingzhouChina

Personalised recommendations