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Genetic local search algorithms for the traveling salesman problem

  • Genetic Algorithms
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Book cover Parallel Problem Solving from Nature (PPSN 1990)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 496))

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Abstract

We briefly review previous attempts to generate near-optimal solutions of the Traveling Salesman Problem by applying Genetic Algorithms. Following the lines of Johnson [1990] we discuss some possibilities for speeding up classical Local Search algorithms by casting them into a genetic frame. In an experimental study two such approaches, viz. Genetic Local Search with 2-Opt neighbourhoods and Lin-Kernighan neighbourhoods, respectively, are compared with the corresponding classical multi-start Local Search algorithms, as well as with Simulated Annealing and Threshold Accepting, using 2-Opt neighbourhoods. As to be expected a genetic organization of Local Search algorithms can considerably improve upon performance though the genetic components alone can hardly counterbalance a poor choice of the neighbourhoods.

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Author information

Authors and Affiliations

  1. Océ-Nederland BV, P.O. Box 101, NL-5900 MA, Venlo

    Nico L. J. Ulder

  2. Philips Research Laboratories, P.O. Box 80.000, NL-5600 JA, Eindhoven

    Emile H. L. Aarts

  3. Eindhoven University of Technology, P.O. Box 513, NL-5600 MB, Eindhoven

    Emile H. L. Aarts

  4. University of Limburg, P.O. Box 616, NL-6200 MD, Maastricht

    Hans-Jürgen Bandelt & Erwin Pesch

  5. Centre for Quantitative Methods, Nederlandse Philips Bedrijven BV, P.O. Box 218, NL-5600 MD, Eindhoven

    Peter J. M. van Laarhoven

Authors
  1. Nico L. J. Ulder
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  2. Emile H. L. Aarts
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  3. Hans-Jürgen Bandelt
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  4. Peter J. M. van Laarhoven
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  5. Erwin Pesch
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Editor information

Hans-Paul Schwefel Reinhard Männer

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© 1991 Springer-Verlag Berlin Heidelberg

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Ulder, N.L.J., Aarts, E.H.L., Bandelt, HJ., van Laarhoven, P.J.M., Pesch, E. (1991). Genetic local search algorithms for the traveling salesman problem. In: Schwefel, HP., Männer, R. (eds) Parallel Problem Solving from Nature. PPSN 1990. Lecture Notes in Computer Science, vol 496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0029740

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  • DOI: https://doi.org/10.1007/BFb0029740

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-54148-6

  • Online ISBN: 978-3-540-70652-6

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