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Assessing Basin Identification Methods for Locating Multiple Optima

In: Advances in Stochastic and Deterministic Global Optimization

Author

Listed:
  • Simon Wessing

    (Technische Universität Dortmund)

  • Günter Rudolph

    (Technische Universität Dortmund)

  • Mike Preuss

    (European Research Center for Information Systems (ERCIS))

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.

Suggested Citation

  • Simon Wessing & Günter Rudolph & Mike Preuss, 2016. "Assessing Basin Identification Methods for Locating Multiple Optima," Springer Optimization and Its Applications, in: Panos M. Pardalos & Anatoly Zhigljavsky & Julius Žilinskas (ed.), Advances in Stochastic and Deterministic Global Optimization, pages 53-70, Springer.
    • Handle: RePEc:spr:spochp:978-3-319-29975-4_4
    DOI: 10.1007/978-3-319-29975-4_4
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