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Potentials of Hyper Populated Ant Colonies

  • Andrzej SiemińskiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9011)

Abstract

The paper discusses the potentials of Hyper Populated Ant Colonies (HPAC) using the well-known Travelling Salesman Problem (TSP) as the study area. The paper starts with an examination of the simple static version of the TSP. The obtained results are later applied to its dynamic version. The carried out experiments strongly suggest that the TSP performance improves significantly with the increase of the Ant Colony size. The phenomena is especially noticeable for dynamic environments. Moreover the processing time does not necessary grow longer. The increasing size of ant colony could be compensated by the decreasing number of iterations. Both the theoretical analysis and initial experiments show that the processing time could be further reduced by the introducing parallelism. The programming technique used is the RMI - Remote Method Invocation.

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References

  1. 1.
    Dorigo, M.: Optimization, Learning and Natural Algorithms, PhD thesis, Politecnico di Milano, Italie, (1992)Google Scholar
  2. 2.
    Dorigo, M., Stuetzle, T.: Ant Colony Optimization: Overview and Recent Advances, IRIDIA - Technical Report Series, Technical Report No. TR/IRIDIA/2009-013, May 2009Google Scholar
  3. 3.
    Chirico, U.: A Java Framework for Ant Colony Systems, Ants2004: Forth International Workshop on Ant Colony Optimization and Swarm Intelligence, Brussels (2004)Google Scholar
  4. 4.
    Siemiński, A.: TSP/ACO Parameter Optimization; Information Systems Architecture and Technology; System Analysis Approach to the Design, Control and Decision Support; pp. 151–161; Oficyna Wydawnicza Politechniki Wrocławskiej 2011Google Scholar
  5. 5.
    Siemiński, A.: Ant colony optimization parameter evaluation. In: Zgrzywa, A., Choroś, K., Siemiński, A. (eds.) Multimedia and Internet Systems: Theory and Practice. AISC, vol. 183, pp. 143–153. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  6. 6.
    Gaertner, D., Clark, K.L.: On Optimal Parameters for Ant Colony Optimization Algorithms. In: IC-AI pp. 83–89, June 2005Google Scholar
  7. 7.
    Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Springer (2003)Google Scholar
  8. 8.
    Busetti, F.: Simulated Annealing Overview, Report (2003)Google Scholar
  9. 9.
    Guntsch, M., Middendorf, M.: Pheromone modification strategies for ant algorithms applied to dynamic TSP. In: Boers, E.J.W., Gottlieb, J., Lanzi, P.L., Smith, R.E., Cagnoni, S., Hart, E., Raidl, G.R., Tijink, H. (eds.) EvoIASP 2001, EvoWorkshops 2001, EvoFlight 2001, EvoSTIM 2001, EvoCOP 2001, and EvoLearn 2001. LNCS, vol. 2037, pp. 213–222. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  10. 10.
    Guntsch, M., Middendorf, M.: A population based approach for ACO. In: Cagnoni, S., Gottlieb, J., Hart, E., Middendorf, M., Raidl, G.R. (eds.) EvoIASP 2002, EvoWorkshops 2002, EvoSTIM 2002, EvoCOP 2002, and EvoPlan 2002. LNCS, vol. 2279, pp. 72–81. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  11. 11.
    Mavrovouniotis, M., Yang, S.: Ant colony optimization with immigrants schemes in dynamic environments. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds.) PPSN XI. LNCS, vol. 6239, pp. 371–380. Springer, Heidelberg (2010)Google Scholar
  12. 12.
    Siemiński, A.: Using ACS for dynamic traveling salesman problem. In: Zgrzywa, A., Choroś, K., Siemiński, A. (eds.) New Research in Multimedia and Internet Systems. AISC, vol. 314, pp. 145–155. Springer, Heidelberg (2015)CrossRefGoogle Scholar
  13. 13.
    Yu, B., Yang, Z.-Z., Xie, J.-X.: A parallel improved ant colony optimization for multi-depot vehicle routing problem. Journal of the Operational Research Society 62, 183–188 (2011)CrossRefGoogle Scholar
  14. 14.
    Doerner, K.F., Hartl, R.F., Benkner, S., Lucka, M.: Parallel cooperative saving based ant colony optimization - multiple search and decomposition approaches. Parallel Processing Letters 16(3), 351–369 (2006)CrossRefMathSciNetGoogle 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

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Faculty of Computer Science and ManagementTechnical University of WrocławWrocławPoland

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