Parallel Ant Colony Optimization for the Quadratic Assignment Problems with Symmetric Multi Processing

  • Shigeyoshi Tsutsui
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5217)


Recently symmetric multi processing (SMP) has become available at a reasonable cost. In this paper, we propose several types of parallel ACO algorithms with SMP for solving the quadratic assignment problem (QAP). These models include the master-slave models and the island models. We evaluated each parallel algorithm with a condition that the run time for each parallel algorithm and the base sequential algorithm are the same. The results suggest that using the master-slave model with increased iteration of ACO algorithms is promising in solving QAPs.


Local Search Parallel Algorithm Parallel Model Quadratic Assignment Problem Island Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Stützle, T.: Parallelization strategies for ant colony optimization. In: 5th International Conf. on Parallel Problem Solving for Nature (PPSN-V), pp. 722–731 (1998)Google Scholar
  2. 2.
    Manfrin, M., Birattari, M., Stützle, T., Dorigo, M.: Parallel ant colony optimization for the traveling salesman problems. In: Ant Colony Optimization and Swarm Intelligence, 5th International Workshop, ANTS 2006, pp. 224–234 (2006)Google Scholar
  3. 3.
    Tsutsui, S.: cAS: Ant colony optimization with cunning ants. In: Proc. of the 9th Int. Conf. on Parallel Problem Solving from Nature (PPSN IX), pp. 162–171 (2006)Google Scholar
  4. 4.
    Tsutsui, S.: Cunning ant system for quadratic assignment problem with local search and parallelization. In: Ghosh, A., De, R.K., Pal, S.K. (eds.) PReMI 2007. LNCS, vol. 4815, pp. 269–278. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  5. 5.
    Randall, M., Lewis, A.: A parallel implementation of ant colony optimization. Journal of Parallel and Distributed Computing 62(9), 1421–1432 (2002)zbMATHCrossRefGoogle Scholar
  6. 6.
    Benkner, S., Doerner, K., Hartl, R., Kiechle, G., Lucka, M.: Communication strategies for parallel cooperative ant colony optimization on clusters and grids. In: Complimentary Proc. of PARA 2004 Workshop on State-of-the-art in Scientific Computing, pp. 3–12 (2005)Google Scholar
  7. 7.
    Bullnheimer, B., Kotsis, G., Strauss, C.: Parallelization strategies for the ant system. High Performance Algorithms and Software in Nonlinear Optimization, 87–100 (1998)Google Scholar
  8. 8.
    Middendorf, M., Reischle, F., Schmeck, H.: Multi colony ant alorithms. Journal of Heuristics 8(3), 3005–3200 (2002)CrossRefGoogle Scholar
  9. 9.
    Lv, Q., Xia, X., Qian, P.: A parallel aco approach based on one pheromone matrix. In: Ant Colony Optimization and Swarm Intelligence, 5th International Workshop, ANT 2006, pp. 332–339 (2006)Google Scholar
  10. 10.
    Talbi, E., Roux, O., Fonlupt, C., Robillard, D.: Parallel ant colonies for the quadratic assignment problem. Generation Computer System 17, 441–449 (2001)zbMATHCrossRefGoogle Scholar
  11. 11.
    Stützle, T., Hoos, H.: MAX-MIN ant system. Future Generation Computer Systems 16(9), 889–914 (2000)CrossRefGoogle Scholar
  12. 12.
    Dorigo, M., Maniezzo, V., Colorni, A.: The ant system: Optimization by a colony of cooperating agents. IEEE Trans. on SMC-Part B 26(1), 29–41 (1996)Google Scholar
  13. 13.
    Taillard, É.D.: The robust taboo search code,
  14. 14.
    Taillard, É.D.: Robust taboo search for the quadratic assignment problem. Parallel Computing 17, 443–455 (1991)CrossRefMathSciNetGoogle Scholar
  15. 15.
    QAPLIB – A Quadratic Assignment Problem Library,

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Shigeyoshi Tsutsui
    • 1
  1. 1.Hannan UniversityMatsubaraJapan

Personalised recommendations