Effective Multi-caste Ant Colony System for Large Dynamic Traveling Salesperson Problems

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8752)

Abstract

Multi-caste ant algorithms allow the coexistence of different search strategies, thereby enhancing search effectiveness in dynamic optimization situation. We present two new variants for a multi-caste ant colony system that promote a better migration of ants between alternative behaviors. Results obtained with large and highly dynamic traveling salesperson instances confirm the effectiveness and robustness of the approach. A detailed analysis reveals that one of the castes should adopt a clearly exploratory behavior, as this minimizes the recovery time after an environmental change.

Keywords

Ant colony optimization Dynamic traveling salesperson problem Multi-caste ant colony system Traffic factor 

References

  1. 1.
    Botee, H., Bonabeau, E.: Evolving ant colony optimization. Adv. Complex Syst. 1(2–3), 149–159 (1998)CrossRefGoogle Scholar
  2. 2.
    Branke, J.: Evolutionary Optimization in Dynamic Environments. Kluwer Academic Publishers, Dordrecht (2002)CrossRefMATHGoogle Scholar
  3. 3.
    Dorigo, M., Gambardella, L.M.: Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans. Evolut. Comput. 1(1), 53–66 (1997)CrossRefGoogle Scholar
  4. 4.
    Dorigo, M., Stützle, T.: Ant Colony Optimization. A Bradford Book. MIT Press, Cambridge (2004)Google Scholar
  5. 5.
    Eyckelhof, C.J., Snoek, M.: Ant systems for a dynamic TSP: ants caught in a traffic jam. In: Dorigo, M., Di Caro, G.A., Sampels, M. (eds.) Ant Algorithms 2002. LNCS, vol. 2463, pp. 88–99. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  6. 6.
    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
  7. 7.
    Guntsch, M., Middendorf, M.: Applying population based ACO to dynamic optimization problems. In: Dorigo, M., Di Caro, G.A., Sampels, M. (eds.) Ant Algorithms 2002. LNCS, vol. 2463, pp. 111–122. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  8. 8.
    Guntsch, M., Middendorf, M., Schmeck, H.: An ant colony optimization approach to dynamic TSP. In: GECCO’01 Proceedings of the Genetic and Evolutionary Computation Conference, pp. 860–867. Morgan Kaufmann Publishers (2001)Google Scholar
  9. 9.
    Hao, Z.F., Cai, R.C., Huang, H.: An adaptive parameter control strategy for ACO. In: Fifth International Conference on Machine Learning and Cybernetics. IEEE Press (2006)Google Scholar
  10. 10.
    Liu, J.: Rank-based ant colony optimization applied to dynamic traveling salesman problems. Eng. Optim. 37(8), 831–847 (2005)MathSciNetCrossRefGoogle 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)CrossRefGoogle Scholar
  12. 12.
    Mavrovouniotis, M., Yang, S.: A memetic ant colony optimization algorithm for the dynamic travelling salesman problem. Soft Comput. 15(7), 1405–1425 (2011)CrossRefGoogle Scholar
  13. 13.
    Mavrovouniotis, M., Yang, S.: An immigrants scheme based on environmental information for ant colony optimization for the dynamic travelling salesman problem. In: Hao, J.-K., Legrand, P., Collet, P., Monmarché, N., Lutton, E., Schoenauer, M. (eds.) EA 2011. LNCS, vol. 7401, pp. 1–12. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  14. 14.
    Mavrovouniotis, M., Yang, S.: Memory-based immigrants for ant colony optimization in changing environments. In: Di Chio, C., Cagnoni, S., Cotta, C., Ebner, M., Ekárt, A., Esparcia-Alcázar, A.I., Merelo, J.J., Neri, F., Preuss, M., Richter, H., Togelius, J., Yannakakis, G.N. (eds.) EvoApplications 2011, Part I. LNCS, vol. 6624, pp. 324–333. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  15. 15.
    Melo, L., Pereira, F., Costa, E.: Multi-caste ant colony optimization algorithms. In: Local Proceedings of EPIA 2011, Lisbon, pp. 978–989 (2011)Google Scholar
  16. 16.
    Melo, L., Pereira, F., Costa, E.: Multi-caste ant colony algorithm for the dynamic traveling salesperson problem. In: Tomassini, M., Antonioni, A., Daolio, F., Buesser, P. (eds.) ICANNGA 2013. LNCS, vol. 7824, pp. 179–188. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  17. 17.
    Reinhelt, G.: \(\{{\rm {TSPLIB}}\}\): a library of sample instances for the TSP (and related problems). http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/
  18. 18.
    Sammoud, O., Solnon, C., Ghedira, K.: A new ACO approach for solving dynamic problems. In: Artificial Evolution (EA’09) - Local Proceedings (2009)Google Scholar
  19. 19.
    Stützle, T.: \(\{{\rm ACOTSP}\}\): software package of various ACO algorithms applied to the symmetric TSP (2002). http://www.aco-metaheuristic.org/aco-code/

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Leonor Melo
    • 1
    • 2
  • Francisco Pereira
    • 1
    • 2
  • Ernesto Costa
    • 2
  1. 1.DEISISEC, Instituto Politécnico de CoimbraQuinta da Nora, CoimbraPortugal
  2. 2.Centro de Informática e Sistemas da Univ. CoimbraCoimbraPortugal

Personalised recommendations