Low-Level Hybridization of Scatter Search and Particle Filter for Dynamic TSP Solving

  • Juan José Pantrigo
  • Abraham Duarte
Part of the Studies in Computational Intelligence book series (SCI, volume 433)


This work presents the application of the Scatter Search Particle Filter (SSPF) algorithm to solve the Dynamic Travelling Salesman Problem (DTSP). SSPF combines sequential estimation and combinatorial optimization methods to efficiently address dynamic optimization problems. SSPF obtains high quality solutions at each time step by taking advantage of the best solutions obtained in the previous ones. To demonstrate the performance of the proposed algorithm, we conduct experiments using two different benchmarks. The first one was generated for us and contains instances sized 25, 50, 75 and 100-cities and the second one are dynamic versions of TSPLIB benchmarks. Experimental results have shown that the performance of SSPF for the DTSP is significantly better than other population based metaheuristics, such as Evolutionary Algorithms or Scatter Search. Our proposal appreciably reduces the execution time without affecting the quality of the obtained results.


Execution Time Problem Instance Particle Filter Travelling Salesman Problem Travel Salesman Problem 
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.


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  1. 1.
    Arulampalam, M., et al.: A Tutorial on Particle Filter for Online Nonlinear/Non-Gaussian Bayesian Tracking. IEEE Trans. on Signal Processing 50(2), 174–188 (2002)CrossRefGoogle Scholar
  2. 2.
    Beasley, J., Sonander, J., Havelock, P.: Scheduling Aircraft Landings at London Heathrow using a Population Heuristic. Journal of the Operational Research Society 52, 483–493 (2001)zbMATHCrossRefGoogle Scholar
  3. 3.
    Beasley, J., Krishnamoorthy, M., Sharaiha, Y., Abramson, D.: The displacement Problem and Dynamically Scheduling Aircraft Landings, Working paper (2002),
  4. 4.
    Blum, C., Roli, A.: Metaheuristics in Combinatorial Optimization: Overview and Conceptual Comparison. ACM Computing Surveys 35(3), 268–308 (2003)CrossRefGoogle Scholar
  5. 5.
    Campos, V., Laguna, M., Marti, R.: Scatter Search for the Linear Ordering Problem. In: New Ideas in Optimization. McGraw-Hill (1999)Google Scholar
  6. 6.
    Carpenter, J., Clifford, P., Fearnhead, P.: Building robust simulation based filters for evolving data sets. Tech. Rep., Dept. Statist., Univ. Oxford, Oxford, U.K. (1999)Google Scholar
  7. 7.
    Dorigo, M., Gambardella, L.: Ant colony system: A cooperative learning approach to the traveling salesman problem. IIEEE Transactions on Evolutionary Computation 1(1), 53–66 (1997)CrossRefGoogle Scholar
  8. 8.
    Dror, M., Powell, W.: Stochastic and Dynamic Models in Transportation. Operations Research 41, 11–14 (1993)CrossRefGoogle Scholar
  9. 9.
    Eyckelhof, C., Snoek, M.: Ant Systems for A Dynamic DSP: Ants Caught in a Traffic Jam. In: Proc. of ANTS 2002 Conference (2002)Google Scholar
  10. 10.
    Glover, F.: A Template for Scatter Search and Path Relinking. In: Hao, J.-K., Lutton, E., Ronald, E., Schoenauer, M., Snyers, D. (eds.) AE 1997. LNCS, vol. 1363, pp. 13–53. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  11. 11.
    Glover, F., Kochenberger, G.: Handbook of metaheuristics. Kluwer Academic Publishers (2002)Google Scholar
  12. 12.
    Gutin, G., Punnen, A.: The traveling salesman problem and its variations. Kluwer Academic Publishers (2004)Google Scholar
  13. 13.
    Guntsh, M., Middendorf, M., Schmeck, H.: An Ant Colony Optimization Approach to Dynamic TSP. In: Proc. GECCO-2001 Conference, pp. 860–867. Morgan Kaufmann Publishers, San Francisco (2000)Google Scholar
  14. 14.
    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
  15. 15.
    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
  16. 16.
    Tao, G., Michalewicz, Z.: Inver-over Operator for the TSP. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol. 1498, pp. 803–812. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  17. 17.
    Karp, R.: Reducibility among Combinatorial Problems. In: Miller, R., Thatcher, J. (eds.) Complexity of Computer Computations, pp. 85–103. Plenum Press (1972)Google Scholar
  18. 18.
    Laguna, M., Marti, R.: Scatter Search methodology and implementations in C. Kluwer Academic Publisher (2003)Google Scholar
  19. 19.
    Larsen, A.: The dynamic vehicle routing problem. PhD Thesis (2000)Google Scholar
  20. 20.
    MacCormick, J.: Stochastic Algorithm for visual tracking. Springer (2002)Google Scholar
  21. 21.
    Michalewitz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. Springer (1996)Google Scholar
  22. 22.
    Pantrigo, J.J., Sánchez, Á., Gianikellis, K., Duarte, A.: Path Relinking Particle Filter for Human Body Pose Estimation. In: Fred, A., Caelli, T.M., Duin, R.P.W., Campilho, A.C., de Ridder, D. (eds.) SSPR&SPR 2004. LNCS, vol. 3138, pp. 653–661. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  23. 23.
    Pantrigo, J.J., Sánchez, A., Montemayor, A.S., Duarte, A.: Multi-Dimensional Visual Tracking Using Scatter Search Particle Filter. Pattern Recognition Letters 29(8), 1160–1174 (2008)CrossRefGoogle Scholar
  24. 24.
    Pantrigo, J.J., Hernández, A., Sánchez, A.: Multiple and Variable Target Visual Tracking for Video Surveillance Applications. Pattern Recognition Letters 31(12), 1577–1590 (2010)CrossRefGoogle Scholar
  25. 25.
    Sadeh, N., Kott, A.: Models and Techniques for Dynamic Demand-Responsive Transportation Planning. Technical Report, CMURI- TR-96-09, Robotics Institute, Carnegie Mellon University (1996)Google Scholar
  26. 26.
    Randall, M.: Constructive Meta-heuristics for Dynamic Optimization Problems. Technical Report, School of Information Technology, Bond University (2002)Google Scholar
  27. 27.
    Reinelt, G.: TSPLIB. University of Heidelberg (1996),
  28. 28.
    Talbi, E.-G.: A Taxonomy of Hybrid Metaheuristics. Journal of Heuristics 8(5), 541–564 (2002)CrossRefGoogle Scholar
  29. 29.
    Vizeacoumar, F.T.: Implementation. Project report Combinatorial Optimization CMPUT - 670 (2003)Google Scholar
  30. 30.
    Zhang-Can, H., Xiao-Lin, H., Si-Duo, C.: Dynamic traveling salesman problem based on evolutionary computation. In: Proceedings of the 2001 Congress on Evolutionary Computation, vol. 2, pp. 1283–1288 (2001)Google Scholar
  31. 31.
    Liu, Z., Kang, L.: A Hybrid Algorithm of n-OPT and GA to Solve Dynamic TSP. In: Li, M., Sun, X.-H., Deng, Q.-n., Ni, J. (eds.) GCC 2003, Part II. LNCS, vol. 3033, pp. 1030–1033. Springer, Heidelberg (2004)CrossRefGoogle Scholar

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© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Universidad Rey Juan CarlosMadridSpain

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