Skip to main content
SpringerLink
Log in

New genetic local search operators for the traveling salesman problem

  • Applications of Evolutionary Computation Evolutionary Computation in Computer Science and Operations Research
  • Conference paper
  • First Online:
Parallel Problem Solving from Nature — PPSN IV (PPSN 1996)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1141))

Included in the following conference series:

Abstract

In this paper, an approach is presented to incorporate problem specific knowledge into a genetic algorithm which is used to compute near-optimum solutions to traveling salesman problems (TSP). The approach is based on using a tour construction heuristic for generating the initial population, a tour improvement heuristic for finding local optima in a given TSP search space, and new genetic operators for effectively searching the space of local optima in order to find the global optimum. The quality and efficiency of solutions obtained for a set of TSP instances containing between 318 and 1400 cities are presented.

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

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. K. Boese, “Cost versus Distance in the Traveling Salesman Problem,” Tech. Rep. TR-950018, UCLA CS Department, 1995.

    Google Scholar 

  2. K.D. Boese and S. Muddu, “A New Adaptive Multi-Start Technique for Combinatorial Global Optimizations,” Operations Research Letters 16, pp. 101–113, 1994.

    Article  Google Scholar 

  3. B. Freisleben and M. Schulte, “Combinatorial Optimization with Parallel Adaptive Threshold Accepting,” in Proceedings of the 1992 European Workshop on Parallel Computing, Barcelona, pp. 176–179. IOS Press, 1992.

    Google Scholar 

  4. B. Freisleben and P. Merz, “A Genetic Local Search Algorithm for Solving Symmetric and Asymmetric Traveling Salesman Problems,” in Proc. 1996 IEEE Int. Conf. on Evolutionary Computation, Nagoya, Japan, pp. 616–621, 1996.

    Google Scholar 

  5. D. E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, 1989.

    Google Scholar 

  6. M. Gorges-Schleuter, “ASPARAGOS: An Asynchronous Parallel Genetic Optimization Strategy,” in Proc. of the 3rd Int. Conf. on Genetic Algorithms, Morgan Kaufmann, 1989.

    Google Scholar 

  7. M. Gorges-Schleuter, “Explicit Parallelism of Genetic Algorithms through Population Structures,” in Parallel Problem Solving from Nature, (H. Schwefel and R. Männer, Eds.), pp. 150–159, Springer-Verlag, 1991.

    Google Scholar 

  8. J. J. Grefenstette, “Incorporating Problem Specific Knowledge into Genetic Algorithms,” in Genetic Algorithms and Simulated Annealing, (L. Davis, ed.), pp. 42–60, Morgan Kaufmann, 1987.

    Google Scholar 

  9. J. Grefenstette, R. Gopal, B. Rosimaita, and D. van Gucht, “ Genetic Algorithms for the Traveling Salesman Problem,” in Proc. of an Int. Conf. on Genetic Algorithms and their Applications, pp. 160–168, 1985.

    Google Scholar 

  10. L. Homaifar, C. Guan, and G. Liepins, “A New Approach to the Traveling Salesman Problem by Genetic Algorithms,” in Proc. of the 5th Int. Conf. on Genetic Algorithms, pp. 460–466, Morgan Kaufmann, 1993.

    Google Scholar 

  11. P. Jog, J. Y. Suh, and D. van Gucht, “The Effects of Population Size, Heuristic Crossover and Local Improvement on a Genetic Algorithm for the Travelling Salesman Problem,” in Proc. of the 3rd Int. Conf. on Genetic Algorithms, pp. 110–115, Morgan Kaufmann, 1989.

    Google Scholar 

  12. D. S. Johnson, “Local Optimization and the Traveling Salesman Problem,” in Annual Int. Colloquium on Automata, Languages and Programming, 1990.

    Google Scholar 

  13. D. S. Johnson and L. A. McGeoch, “The Traveling Salesman Problem: A Case Study in Local Optimization,” in (E. H. L. Aarts and J. K. Lenstra, eds.) Local Search in Combinatorial Optimization, Wiley & Sons, New York, to appear.

    Google Scholar 

  14. P. van Laarhoven and E. H. L. Aarts, Simulated Annealing: Theory and Applications. Kluwer Academic Publ., 1987.

    Google Scholar 

  15. E. L. Lawler, J. K. Lenstra and D. B. Shmoys, The Traveling Salesman Problem: A Guided Tour of Combinatorial Optimization. Wiley and Sons, New York, 1985.

    Google Scholar 

  16. S. Lin, “Computer Solutions of the Travelling Salesman Problem,” Bell System Tech. Journal, Vol. 44, pp. 2245–2269, 1965.

    Google Scholar 

  17. S. Lin and B. Kernighan, “An Effective Heuristic Algorithm for the Traveling Salesman Problem,” Operations Research, Vol. 21, pp. 498–516, 1973.

    Google Scholar 

  18. K. Mathias and D. Whitley, “Genetic Operators, the Fitness Landscape and the Traveling Salesman Problem,” in Parallel Problem Solving from Nature, (R. Männer and B. Manderick, Eds.), pp. 219–228, Elsevier, 1992.

    Google Scholar 

  19. H. Mühlenbein, “Parallel Genetic Algorithms, Population Genetics and Combinatorial Optimization,” in Proc. of the 3rd Int. Conf. on Genetic Algorithms, (J. D. Schaffer, ed.), pp. 416–421, Morgan Kaufmann, 1989.

    Google Scholar 

  20. H. Mühlenbein, “Evolution in Time and Space — The Parallel Genetic Algorithm,” in Foundations of Genetic Algorithms, (G. J. E. Rawlins, ed.), M. Kaufmann, 1991.

    Google Scholar 

  21. H. Mühlenbein, M. Gorges-Schleuter, and O. Krämer, “Evolution Algorithms in Combinatorial Optimization,” Parallel Computing, Vol. 7, pp. 65–88, 1988.

    Article  Google Scholar 

  22. M. Padberg and G. Rinaldi, “Optimization of a 532-city Symmetric Traveling Salesman Problem by Branch & Cut,” Operations Research Lett. 6, pp. 1–7, 1987.

    Article  Google Scholar 

  23. G. Reinelt, “TSPLIB — A Traveling Salesman Problem Library,” ORSA Journal on Computing, Vol. 3, No. 4, pp. 376–384, 1991.

    Google Scholar 

  24. G. Reinelt, The Traveling Salesman: Computational Solutions for TSP Applications. Vol. 840 of Lecture Notes in Computer Science, Springer-Verlag, 1994.

    Google Scholar 

  25. J. Y. Suh and D. van Gucht, “Incorporating Heuristic Information into Genetic Search,” in Proc. of the 2nd Int. Conf. on Genetic Algorithms, pp. 100–107, Lawrence Erlbaum, 1987.

    Google Scholar 

  26. A. Y. C. Tang and K. S. Leung, “A Modified Edge Recombination Operator for the Travelling Salesman Problem,” in Parallel Problem Solving from Nature, (H.-P. Schwefel and R. Männer, Eds.), pp. 180–188, Springer-Verlag, 1994.

    Google Scholar 

  27. N. L. J. Ulder, E. H. L. Aarts, H. J. Bandelt, P. J. M. van Laarhoven, and E. Pesch, “Genetic Local Search Algorithms for the Traveling Salesman Problem,” in Parallel Problem Solving from Nature, (H. P. Schwefel and R. Männer, Eds.), pp. 109–116, Springer-Verlag, 1991.

    Google Scholar 

  28. M. Wall, “GALIB 2.3.2,” http://lancet.mit.edu/ga, 1995.

    Google Scholar 

  29. D. Whitley, T. Starkweather, and D. Fuquay, “Scheduling Problems and Traveling Salesman: The Genetic Edge Recombination Operator,” in Proc. of the 3rd Int. Conf. on Genetic Algorithms, pp. 133–140, Morgan Kaufmann, 1989.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical Engineering and Computer Science (FB 12), University of Siegen, Hölderlinstr. 3, D-57068, Siegen, Germany

    Bernd Freisleben & Peter Merz

Authors
  1. Bernd Freisleben
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Peter Merz
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Hans-Michael Voigt Werner Ebeling Ingo Rechenberg Hans-Paul Schwefel

Rights and permissions

Reprints and permissions

Copyright information

© 1996 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Freisleben, B., Merz, P. (1996). New genetic local search operators for the traveling salesman problem. In: Voigt, HM., Ebeling, W., Rechenberg, I., Schwefel, HP. (eds) Parallel Problem Solving from Nature — PPSN IV. PPSN 1996. Lecture Notes in Computer Science, vol 1141. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61723-X_1052

Download citation

  • DOI: https://doi.org/10.1007/3-540-61723-X_1052

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-61723-5

  • Online ISBN: 978-3-540-70668-7

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation