Gradient-Controlled, Typical-Distance Clustering for Global Optimization

  • Isaac Lagaris
  • I.G. Tsoulos
    • Categories Global Optimization, Stochastic Approaches Tags , , Short URL: https://optimization-online.org/?p=9535

    We present a stochastic global optimization method that employs a clustering technique which is based on a typical distance and a gradient test. The method aims to recover all the local minima inside a rectangular domain. A new stopping rule is used. Comparative results on a set of test functions are reported.

    Citation

    Preprint, no 4-5/2004 Dept. of Computer Science, University of Ioannina, Greece

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