Global optimization by monotonic transformation

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Abstract

This paper addresses the problem of global optimization by means of a monotonic transformation. With an observation on global optimality of functions under such a transformation, we show that a simple and effective algorithm can be derived to search within possible regions containing the global optima. Numerical experiments are performed to compare this algorithm with one that does not incorporate transformed information using several benchmark problems. These results are also compared to best known global search algorithms in the literature. In addition, the algorithm is shown to be useful for several neural network learning problems, which possess much larger parameter spaces.

Original languageEnglish
Pages (from-to)77-99
Number of pages23
JournalComputational Optimization and Applications
Volume23
Issue number1
DOIs
Publication statusPublished - 2002 Oct 1

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All Science Journal Classification (ASJC) codes

  • Control and Optimization
  • Computational Mathematics
  • Applied Mathematics

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