Ant Colony System with a Restart Procedure for TSP

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9876)

Abstract

Ant Colony Optimization has proven to be an efficient optimization technique for solving difficult optimization problems. Nonetheless, the convergence of the ACO can still be prohibitively slow. We investigate how the recently proposed Restart Procedure (RP) can be used to improve convergence of the Ant Colony System (ACS) algorithm, which is among the most often applied algorithms from the ACO family. In particular, we present a series of computational experiments to answer the question about how the values of the RP-related parameters influence the convergence of the ACS combined with the RP (ACS-RP). We also show that the ACS-RP achieves significantly better results than the standard ACS within the same computational budget.

Keywords

Ant Colony System Restart procedure Travelling salesman problem 

References

  1. 1.
    Blum, C.: Beam-aco–hybridizing ant colony optimization with beam search: an application to open shop scheduling. Comput. Oper. Res. 32(6), 1565–1591 (2005)CrossRefMATHGoogle Scholar
  2. 2.
    Blum, C., Dorigo, M.: The hyper-cube framework for ant colony optimization. IEEE Trans. Syst. Man Cybern. Part B: Cybern. 34(2), 1161–1172 (2004)CrossRefGoogle Scholar
  3. 3.
    Carvelli, L.: Improving convergence of combinatorial optimization meta-heuristic algorithms. Ph.D. thesis, Sapienza Universita di Roma, Facolta di Scienze Matematiche Fisiche e Naturali (2013)Google Scholar
  4. 4.
    Dorigo, M., Gambardella, L.M.: Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans. Evol. Comput. 1(1), 53–66 (1997)CrossRefGoogle Scholar
  5. 5.
    Dorigo, M., Stützle, T.: Ant Colony Optimization. MIT Press, Cambridge (2004)MATHGoogle Scholar
  6. 6.
    Feo, T.A., Resende, M.G.: Greedy randomized adaptive search procedures. J. Global Optim. 6(2), 109–133 (1995)MathSciNetCrossRefMATHGoogle Scholar
  7. 7.
    Glover, F., Laguna, M.: Tabu Search. Springer, New York (2013)MATHGoogle Scholar
  8. 8.
    Glover, F., Laguna, M., Martí, R.: Fundamentals of scatter search and path relinking. Control Cybern. 29(3), 653–684 (2000)MathSciNetMATHGoogle Scholar
  9. 9.
    Glover, F.W., Kochenberger, G.A.: Handbook of Metaheuristics, vol. 57. Springer, New York (2006)MATHGoogle Scholar
  10. 10.
    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.) EvoWorkshops 2001. LNCS, vol. 2037, p. 213. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  11. 11.
    Martí, R., Resende, M.G.C., Ribeiro, C.C.: Multi-start methods for combinatorial optimization. Eur. J. Oper. Res. 226(1), 1–8 (2013)MathSciNetCrossRefMATHGoogle Scholar
  12. 12.
    Oliveira, S.M., Hussin, M.S., Stützle, T., Roli, A., Dorigo, M.: A detailed analysis of the population-based ant colony optimization algorithm for the TSP and the QAP. In: Krasnogor, N., Lanzi, P.L. (eds.) GECCO 2011, Companion Material Proceedings, Dublin, Ireland, 12–16 July, pp. 13–14. ACM (2011)Google Scholar
  13. 13.
    R. Skinderowicz. The GPU-based parallel ant colony system. J. Parallel Distrib. Comput. (2016, in press)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Intitute of Computer ScienceUniversity of SilesiaSosnowiecPoland

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