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Parallel deterministic and stochastic global minimization of functions with very many minima

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Abstract

The optimization of three problems with high dimensionality and many local minima are investigated under five different optimization algorithms: DIRECT, simulated annealing, Spall’s SPSA algorithm, the KNITRO package, and QNSTOP, a new algorithm developed at Indiana University.

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Authors and Affiliations

  1. Department of Computer Science, Virginia Polytechnic Institute, State University, Blacksburg, VA, 24061, USA

    David R. Easterling

  2. Departments of Computer Science and Mathematics, Virginia Polytechnic Institute, State University, Blacksburg, VA, 24061, USA

    Layne T. Watson

  3. Department of Engineering Science and Mechanics, Virginia Polytechnic Institute, State University, Blacksburg, VA, 24061, USA

    Michael L. Madigan

  4. School of Informatics and Computing, Indiana University, Bloomington, IN, 47405, USA

    Brent S. Castle

  5. Department of Statistics, Indiana University, Bloomington, IN, 47405, USA

    Michael W. Trosset

Authors
  1. David R. Easterling
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  2. Layne T. Watson
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  3. Michael L. Madigan
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  4. Brent S. Castle
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  5. Michael W. Trosset
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Corresponding author

Correspondence to David R. Easterling.

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Easterling, D.R., Watson, L.T., Madigan, M.L. et al. Parallel deterministic and stochastic global minimization of functions with very many minima. Comput Optim Appl 57, 469–492 (2014). https://doi.org/10.1007/s10589-013-9592-1

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