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Assessing Basin Identification Methods for Locating Multiple Optima

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Book cover Advances in Stochastic and Deterministic Global Optimization

Part of the book series: Springer Optimization and Its Applications ((SOIA,volume 107))

Abstract

Basin identification is an important ingredient in global optimization algorithms for the efficient use of local searches. An established approach for this task is obtaining topographical information about the objective function from a discrete sample of the search space and representing it in a graph structure. So far, different variants of this approach are usually assessed by evaluating the performance of a whole optimization algorithm using them as components. In this work, we compare two approaches on their own, namely topographical selection and nearest-better clustering, regarding their ability to identify the distinct attraction basins of multimodal functions. We show that both have different strengths and weaknesses, as their behavior is very dependent on the problem instance.

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Authors and Affiliations

  1. Department of Computer Science, Technische Universität Dortmund, Dortmund, 44227, Germany

    Simon Wessing & Günter Rudolph

  2. European Research Center for Information Systems (ERCIS), WWU Münster, Münster, 48149, Germany

    Mike Preuss

Authors
  1. Simon Wessing
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  2. Günter Rudolph
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  3. Mike Preuss
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Correspondence to Simon Wessing .

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Editors and Affiliations

  1. Department of Industrial and Systems Engineering, University of Florida, Gainesville, Florida, USA

    Panos M. Pardalos

  2. Cardiff School of Mathematics, Cardiff University, Cardiff, United Kingdom

    Anatoly Zhigljavsky

  3. Institute of Mathematics and Informatics, Vilnius University, Vilnius, Lithuania

    Julius Žilinskas

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Wessing, S., Rudolph, G., Preuss, M. (2016). Assessing Basin Identification Methods for Locating Multiple Optima. In: Pardalos, P., Zhigljavsky, A., Žilinskas, J. (eds) Advances in Stochastic and Deterministic Global Optimization. Springer Optimization and Its Applications, vol 107. Springer, Cham. https://doi.org/10.1007/978-3-319-29975-4_4

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