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Multi-Swarm Optimization for Dynamic Combinatorial Problems: A Case Study on Dynamic Vehicle Routing Problem

  • Mostepha Redouane Khouadjia
  • Enrique Alba
  • Laetitia Jourdan
  • El-Ghazali Talbi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6234)

Abstract

Many combinatorial real-world problems are mostly dynamic. They are dynamic in the sense that the global optimum location and its value change over the time, in contrast to static problems. The task of the optimization algorithm is to track this shifting optimum. Particle Swarm Optimization (PSO) has been previously used to solve continuous dynamic optimization problems, whereas only a few works have been proposed for combinatorial ones. One of the most interesting dynamic problems is the Dynamic Vehicle Routing Problem (DVRP). This paper presents a Multi-Adaptive Particle Swarm Optimization (MAPSO) for solving the Vehicle Routing Problem with Dynamic Requests (VRPDR). In this approach the population of particles is split into a set of interacting swarms. Such a multi-swarm helps to maintain population diversity and good tracking is achieved. The effectiveness of this approach is tested on a well-known set of benchmarks, and compared to other metaheuristics from literature. The experimental results show that our multi-swarm optimizer significantly outperforms single solution and population based metaheuristics on this problem.

Keywords

Particle Swarm Optimization Geographic Information System Vehicle Route Problem Explicit Memory Dynamic Optimization 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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References

  1. 1.
    Ai, T.J., Kachitvichyanukul, V.: A particle swarm optimization for the vehicle routing problem with simultaneous pickup and delivery. Comput. Oper. Res. 36(5), 1693–1702 (2009)zbMATHCrossRefGoogle Scholar
  2. 2.
    Alba, E.: Parallel metaheuristics: a New Class of Algorithms. Wiley Interscience, Hoboken (2005)zbMATHCrossRefGoogle Scholar
  3. 3.
    Bent, R., Van Hentenryck, P.: Online Stochastic and Robust Optimization. In: Maher, M.J. (ed.) ASIAN 2004. LNCS, vol. 3321, p. 286. Springer, Heidelberg (2004)Google Scholar
  4. 4.
    Blackwell, T., Branke, J.: Multi-swarm Optimization in Dynamic Environments. In: Raidl, G.R., Cagnoni, S., Branke, J., Corne, D.W., Drechsler, R., Jin, Y., Johnson, C.G., Machado, P., Marchiori, E., Rothlauf, F., Smith, G.D., Squillero, G. (eds.) EvoWorkshops 2004. LNCS, vol. 3005, pp. 489–500. Springer, Heidelberg (2004)Google Scholar
  5. 5.
    Blackwell, T., Branke, J.: Multiswarms, exclusion, and anti-convergence in dynamic environments. IEEE transactions on evolutionary computation 10(4), 459–472 (2006)CrossRefGoogle Scholar
  6. 6.
    Blackwell, T.: Particle swarm optimization in dynamic environments. In: Evolutionary Computation in Dynamic and Uncertain Environments, pp. 29–49. Springer, Berlin (2007)CrossRefGoogle Scholar
  7. 7.
    Branke, J.: Memory enhanced evolutionary algorithms for changing optimization problems. In: Proceedings of the IEEE Congress on Evolutionary Computation, vol. 3, pp. 1875–1882. IEEE Press, Washington (1999)Google Scholar
  8. 8.
    Branke, J.: Evolutionary Optimization in Dynamic Environments. Kluwer Academic Publishers, Norwell (2001)Google Scholar
  9. 9.
    Conover, W.: Practical nonparametric statistics. Wiley, New York (1999)Google Scholar
  10. 10.
    Dantzig, G., Ramser, J.: The truck dispatching problem. Operations Research, Management Sciences 6(1), 80–91 (1959)zbMATHMathSciNetGoogle Scholar
  11. 11.
    Hanshar, F., Ombuki-Berman, B.: Dynamic vehicle routing using genetic algorithms. Applied Intelligence 27, 89–99 (2007)zbMATHCrossRefGoogle Scholar
  12. 12.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks, vol. 4, pp. 1942–1948. IEEE Service Center, Piscataway (1995)CrossRefGoogle Scholar
  13. 13.
    Kilby, P., Prosser, P., Shaw, P.: Dynamic VRPs: A study of scenarios. In: APES-06-1998, University of Strathclyde, U.K (1998)Google Scholar
  14. 14.
    Li, X., Branke, J., Blackwell, T.: Particle swarm with speciation and adaptation in a dynamic environment. In: GECCO 2006: Proceedings of the 8th annual conference on Genetic and Evolutionary Computation, pp. 51–58. ACM, New York (2006)CrossRefGoogle Scholar
  15. 15.
    Lin, S.: Computer solutions of the traveling salesman problem. Bell System Computer Journal 44, 2245–2269 (1965)zbMATHGoogle Scholar
  16. 16.
    Montemanni, R., Gambardella, L., Rizzoli, A., Donati, A.: A new algorithm for a dynamic vehicle routing problem based on ant colony system. Journal of Combinatorial Optimization 10, 327–343 (2005)zbMATHCrossRefMathSciNetGoogle Scholar
  17. 17.
    Oppacher, F., Wineberg, M.: The shifting balance genetic algorithm: Improving the GA in a dynamic environment. In: Proceedings of the Genetic and Evolutionary Computation Conference, vol. 1, pp. 504–510 (1999)Google Scholar
  18. 18.
    Parrott, D., Li, X.: A particle swarm model for tracking multiple peaks in a dynamic environment using speciation. In: Congress on Evolutionary Computation (CEC 2004), vol. 1 (2004)Google Scholar
  19. 19.
    Psaraftis, H.: Dynamic vehicle routing: status and prospects. Annals of Opertations Reasearch 61, 143–164 (1995)zbMATHCrossRefGoogle Scholar
  20. 20.
    Rego, C.: Node-ejection chains for the vehicle routing problem: Sequential and parallel algorithms. Parallel Computing 27(3), 201–222 (2001)zbMATHCrossRefGoogle Scholar
  21. 21.
    Talbi, E.: Metaheuristics: from design to implementation. Wiley, Chichester (2009)zbMATHGoogle Scholar
  22. 22.
    Ursem, R.: Multinational GAs: Multimodal optimization techniques in dynamic environments. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 19–26. Morgan Kaufmann, San Francisco (2000)Google Scholar
  23. 23.
    Yang, S.: Explicit memory schemes for evolutionary algorithms in dynamic environments. In: Evolutionary Computation in Dynamic and Uncertain Environments, pp. 3–28. Springer, Berlin (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Mostepha Redouane Khouadjia
    • 1
  • Enrique Alba
    • 2
  • Laetitia Jourdan
    • 1
  • El-Ghazali Talbi
    • 1
  1. 1.National Institute for Research in Computer Science and Control (INRIA) LilleFrance
  2. 2.Departamento de Lenguajes y Ciencias de la ComputaciónUniversidad de Málaga, E.T.S. Ingeniería InformáticaMálagaSpain

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