An Ant-Based Heuristic for the Railway Traveling Salesman Problem

  • Petrica C. Pop
  • Camelia M. Pintea
  • Corina Pop Sitar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4448)


We consider the Railway Traveling Salesman Problem, denoted RTSP, in which a salesman using the railway network wishes to visit a certain number of cities to carry out his/her business, starting and ending at the same city, and having the goal to minimize the overall time of the journey. The RTSP is NP-hard and it is related to the Generalized Traveling Salesman Problem. In this paper we present an effective meta-heuristic based on ant colony optimization (ACO) for solving the RTSP. Computational results are reported for real-world and synthetic data. The results obtained demonstrate the superiority of the proposed algorithm in comparison with the existing method.


Synthetic Data Travel Salesman Problem Tabu List Good Tour Elementary Connection 
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.
    Bonabeau, E., Dorigo, M., Tehraulaz, G.: Swarm intelligence from natural to artificial systems. Oxford University Press, Oxford, UK (1999)zbMATHGoogle Scholar
  2. 2.
    Dorigo, M., Di Caro, G.: The ant colony optimization meta-heuristic. In: Corne, D., Dorigo, M., Glover, F. (eds.) New ideas in optimization, pp. 11–32. McGraw-Hill, London (1999)Google Scholar
  3. 3.
    Dorigo, M., Di Caro, G., Gambardella, L.M.: Ant algorithms for discrete optimization. Artificial Life 5(2), 137–172 (1999)CrossRefGoogle Scholar
  4. 4.
    Dorigo, M., Gambardella, L.M.: Ant Colony System: A cooperative learning approach to the Traveling Salesman Problem. IEEE Trans. Evol. Comp. 1, 53–66 (1997)CrossRefGoogle Scholar
  5. 5.
    Fischetti, M., Salazar, J.J., Toth, P.: The symmetric generalized traveling salesman polytope, Networks, 26, pp. 113–123 (1995)Google Scholar
  6. 6.
    Fischetti, M., Salazar, J.J., Toth, P.: A branch-and-cut algorithm for the symmetric generalized traveling salesman problem, Operations Research, vol. 45, pp. 378–394 (1997)Google Scholar
  7. 7.
    Hadjicharalambous, G., Pop, P., Pyrga, E., Tsaggouris, G., Zaroliagis, C.: The Railway Traveling Salesman Problem, in Algorithmic Methods for Railway Optimization. In: Hadjicharalambous, G., Pop, P., Pyrga, E., Tsaggouris, G., Zaroliagis, C. (eds.) Proc. ATMOS 2004, to appear. Lecture Notes in Computer Science, Springer, Berlin Heidelberg New York (2004)Google Scholar
  8. 8.
    Pintea, C-M., Pop, P.C., Chira, C.: Reinforcing Ant Colony System for the Generalized Traveling Salesman Problem, In: Proceedings of the First International Conference Bio-Inspired Computing: Theory and Applications, pp. 245–252 (2006)Google Scholar
  9. 9.
    Schultz, F., Wagner, D., Weihe, K.: Dijkstra’s Algorithm On-line: An Empirical Case Study from the Public Railroad Transport, ACM Journal of Experimental Algorithmics, vol. 5:(12) (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Petrica C. Pop
    • 1
  • Camelia M. Pintea
    • 2
  • Corina Pop Sitar
    • 3
  1. 1.Department of Mathematics and Computer Science, Faculty of Sciences, North University of Baia MareRomania
  2. 2.Faculty of Mathematics and Computer Science, Babes-Bolyai University, Cluj-NapocaRomania
  3. 3.Faculty of Economics, Babes-Bolyai University of Cluj-NapocaRomania

Personalised recommendations