Advertisement

Parallel Ant Colony Optimization Algorithm on a Multi-core Processor

  • Shigeyoshi Tsutsui
  • Noriyuki Fujimoto
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6234)

Abstract

This paper proposes parallelization methods of ACO algorithms on a computing platform with a multi-core processor aiming at fast execution to find acceptable solutions. As an ACO algorithm, we use the cunning Ant System and test on several sizes of TSP instances. As the parallelization method, we use agent level parallelization in one colony using Java thread programming. According to the synchronization and exclusive control modes among threads, we propose three types of parallel ACO algorithms. Among them, that which we call the rough asynchronous parallel model shows the most promising results.

Keywords

Local Search Candidate Solution Computing Platform Solution Construction Fast Execution 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Applegate, D., et al.: ANSI C code as gzipped tar file, Concorde TSP solver (2006), http://www.tsp.gatech.edu/concorde.html
  2. 2.
    Benkner, S., Doerner, K., Hartl, R., Kiechle, G., Lucka, M.: Communication strategies for parallel cooperative ant colony optimization on clusters and grids. In: Dongarra, J., Madsen, K., Waśniewski, J. (eds.) PARA 2004. LNCS, vol. 3732, pp. 3–12. Springer, Heidelberg (2006)Google Scholar
  3. 3.
    Lv, Q., Xia, X., Qian, P.: A parallel aco approach based on one pheromone matrix. In: Dorigo, M., Gambardella, L.M., Birattari, M., Martinoli, A., Poli, R., Stützle, T. (eds.) ANTS 2006. LNCS, vol. 4150, pp. 332–339. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  4. 4.
    Manfrin, M., Birattari, M., Stützle, T., Dorigo, M.: Parallel ant colony optimization for the traveling salesman problems. 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
  5. 5.
    Stützle, T., Hoos, H.: Max-min ant system. Future Generation Computer Systems 16(9), 889–914 (2000)CrossRefGoogle Scholar
  6. 6.
    Stützle, T.: Parallelization strategies for ant colony optimization. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol. 1498, pp. 722–731. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  7. 7.
    Tsutsui, S.: cAS: Ant colony optimization with cunning ants. In: Ebeling, W., Rechenberg, I., Voigt, H.-M., Schwefel, H.-P. (eds.) PPSN 1996. LNCS, vol. 1141, pp. 162–171. Springer, Heidelberg (1996)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Shigeyoshi Tsutsui
    • 1
  • Noriyuki Fujimoto
    • 2
  1. 1.Management InformationHannan UniversityMatsubaraJapan
  2. 2.ScienceOsaka Prefecture UniversitySakaiJapan

Personalised recommendations