Skip to main content

Combinatorial Neighborhood Topology Particle Swarm Optimization Algorithm for the Vehicle Routing Problem

  • Conference paper
Evolutionary Computation in Combinatorial Optimization (EvoCOP 2013)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7832))

Abstract

One of the main problems in the application of a Particle Swarm Optimization in combinatorial optimization problems, especially in routing type problems like the Traveling Salesman Problem, the Vehicle Routing Problem, etc., is the fact that the basic equation of the Particle Swarm Optimization algorithm is suitable for continuous optimization problems and the transformation of this equation in the discrete space may cause loose of information and may simultaneously need a large number of iterations and the addition of a powerful local search algorithm in order to find an optimum solution. In this paper, we propose a different way to calculate the position of each particle which will not lead to any loose of information and will speed up the whole procedure. This was achieved by replacing the equation of positions with a novel procedure that includes a Path Relinking Strategy and a different correspondence of the velocities with the path that will follow each particle. The algorithm is used for the solution of the Capacitated Vehicle Routing Problem and is tested in the two classic set of benchmark instances from the literature with very good results.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 49.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Ai, T.J., Kachitvichyanukul, V.: Particle swarm optimization and two solution representations for solving the capacitated vehicle routing problem. Computers and Industrial Engineering 56, 380–387 (2009)

    Article  Google Scholar 

  2. Christofides, N., Mingozzi, A., Toth, P.: The vehicle routing problem. In: Christofides, N., Mingozzi, A., Toth, P., Sandi, C. (eds.) Combinatorial Optimization. Wiley, Chichester (1979)

    Google Scholar 

  3. Clerc, M., Kennedy, J.: The particle swarm: explosion, stability and convergence in a multi-dimensional complex space. IEEE Transactions on Evolutionary Computation 6, 58–73 (2002)

    Article  Google Scholar 

  4. Glover, F., Laguna, M., Marti, R.: Scatter search and path relinking: Advances and applications. In: Glover, F., Kochenberger, G.A. (eds.) Handbook of Metaheuristics, pp. 1–36. Kluwer Academic Publishers, Boston (2003)

    Google Scholar 

  5. Goksal, F.P., Karaoglan, I., Altiparmak, F.: A hybrid discrete particle swarm optimization for vehicle routing problem with simultaneous pickup and delivery. Computers and Industrial Engineering (2012), doi:10.1016/j.cie.2012.01.005

    Google Scholar 

  6. Golden, B.L., Wasil, E.A., Kelly, J.P., Chao, I.M.: The impact of metaheuristics on solving the vehicle routing problem: algorithms, problem sets, and computational results. In: Crainic, T.G., Laporte, G. (eds.) Fleet Management and Logistics, pp. 33–56. Kluwer Academic Publishers, Boston (1998)

    Chapter  Google Scholar 

  7. Golden, B., Raghavan, S., Wasil, E.: The Vehicle Routing Problem: Latest Advances and New Challenges. Springer LLC (2008)

    Google Scholar 

  8. Hansen, P., Mladenovic, N.: Variable neighborhood search: Principles and applications. European Journal of Operational Research 130, 449–467 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  9. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of 1995 IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)

    Google Scholar 

  10. Kim, B.I., Song, S.J.: A probability matrix based particle swarm optimization for the capacitated vehicle routing problem. Journal Intelligence Manufacturing 23, 1119–1126 (2012)

    Article  Google Scholar 

  11. Marinakis, Y., Marinaki, M.: A Particle Swarm Optimization Algorithm with Path Relinking for the Location Routing Problem. Journal of Mathematical Modelling and Algorithms 7(1), 59–78 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  12. Marinakis, Y., Marinaki, M.: A Hybrid Multi-Swarm Particle Swarm Optimization Algorithm for the Probabilistic Traveling Salesman Problem. Computers and Operations Research 37, 432–442 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  13. Marinakis, Y., Marinaki, M.: A Hybrid Genetic - Particle Swarm Optimization Algorithm for the Vehicle Routing Problem. Expert Systems with Applications 37, 1446–1455 (2010)

    Article  MathSciNet  Google Scholar 

  14. Marinakis, Y., Marinaki, M.: A Hybrid Particle Swarm Optimization Algorithm for the Open Vehicle Routing Problem. In: Dorigo, M., Birattari, M., Blum, C., Christensen, A.L., Engelbrecht, A.P., Groß, R., Stützle, T. (eds.) ANTS 2012. LNCS, vol. 7461, pp. 180–187. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  15. Marinakis, Y., Marinaki, M., Dounias, G.: A Hybrid Particle Swarm Optimization Algorithm for the Vehicle Routing Problem. Engineering Applications of Artificial Intelligence 23, 463–472 (2010)

    Article  Google Scholar 

  16. Mester, D., Braysy, O.: Active guided evolution strategies for large scale capacitated vehicle routing problems. Computers and Operations Research 34, 2964–2975 (2007)

    Article  MATH  Google Scholar 

  17. Pisinger, D., Ropke, S.: A general heuristic for vehicle routing problems. Computers and Operations Research 34, 2403–2435 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  18. Prins, C.: A simple and effective evolutionary algorithm for the vehicle routing problem. Computers and Operations Research 31, 1985–2002 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  19. Prins, C.: A GRASP × Evolutionary Local Search Hybrid for the Vehicle Routing Problem. In: Pereira, F.B., Tavares, J. (eds.) Bio-inspired Algorithms for the Vehicle Routing Problem. SCI, vol. 161, pp. 35–53. Springer, Heideberg (2009)

    Chapter  Google Scholar 

  20. Reimann, M., Doerner, K., Hartl, R.F.: D-Ants: savings based ants divide and conquer the vehicle routing problem. Computers and Operations Research 31(4), 563–591 (2004)

    Article  MATH  Google Scholar 

  21. Rochat, Y., Taillard, E.D.: Probabilistic diversification and intensification in local search for vehicle routing. Journal of Heuristics 1, 147–167 (1995)

    Article  MATH  Google Scholar 

  22. Rosendo, M., Pozo, A.: A hybrid Particle Swarm Optimization algorithm for combinatorial optimization problems. In: 2010 IEEE Congress on Evolutionary Computation, CEC (2010), doi:10.1109/CEC.2010.5586178

    Google Scholar 

  23. Tarantilis, C.D., Kiranoudis, C.T.: BoneRoute: an adaptive memory-based method for effective fleet management. Annals of Operations Research 115(1), 227–241 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  24. Toth, P., Vigo, D.: The vehicle routing problem. Monographs on Discrete Mathematics and Applications. SIAM (2002)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Marinakis, Y., Marinaki, M. (2013). Combinatorial Neighborhood Topology Particle Swarm Optimization Algorithm for the Vehicle Routing Problem. In: Middendorf, M., Blum, C. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2013. Lecture Notes in Computer Science, vol 7832. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37198-1_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-37198-1_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37197-4

  • Online ISBN: 978-3-642-37198-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics