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Optimizing complex problems by nature's algorithms: Simulated annealing and evolution strategy—a comparative study

  • Comparison Of Problem Solving Strategies From Nature
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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 compare two optimization algorithms which glean their heuristic from nature: simulated annealing and evolution strategy. These algorithms are applied to difficult optimization problems: finding binary sequences with low autocorrelation, calculating ground states of certain spin glass Hamiltonians, and giving the optimal tour in a traveling salesman problem. Our findings show a problem dependence of the quality of the results. Because of fundamental difficulties in the judgement of the algorithms' quality no final conclusion can be drawn, but the comparison gives valuable insight in the behaviour of the algorithms.

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Authors and Affiliations

  1. Interdisziplinäres Projektzentrum für Supercomputing ETH-Zentrum, CH-8092, Zürich

    Claas de Groot & Diethelm Würtz

  2. Institut für Theoretische Physik, Universität Heidelberg, D-6900, Heidelberg

    Karl Heinz Hoffmann

  3. Institut für Theoretische Physik, ETH-Hönggerberg, Zürich

    Claas de Groot & Diethelm Würtz

Authors
  1. Claas de Groot
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  2. Diethelm Würtz
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  3. Karl Heinz Hoffmann
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Editor information

Hans-Paul Schwefel Reinhard Männer

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© 1991 Springer-Verlag Berlin Heidelberg

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de Groot, C., Würtz, D., Hoffmann, K.H. (1991). Optimizing complex problems by nature's algorithms: Simulated annealing and evolution strategy—a comparative study. 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/BFb0029786

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  • DOI: https://doi.org/10.1007/BFb0029786

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-54148-6

  • Online ISBN: 978-3-540-70652-6

  • eBook Packages: Springer Book Archive

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