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Continuous GRASP with a local active-set method for bound-constrained global optimization

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Abstract

Global optimization seeks a minimum or maximum of a multimodal function over a discrete or continuous domain. In this paper, we propose a hybrid heuristic—based on the CGRASP and GENCAN methods—for finding approximate solutions for continuous global optimization problems subject to box constraints. Experimental results illustrate the relative effectiveness of CGRASP–GENCAN on a set of benchmark multimodal test functions.

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Author information

Authors and Affiliations

  1. Instituto de Matemática e Estatística, Universidade de São Paulo, São Paulo, Brazil

    Ernesto G. Birgin & Erico M. Gozzi

  2. Algorithms and Optimization Research Department, AT&T Labs Research, Florham Park, NJ, USA

    Mauricio G. C. Resende

  3. Department of Computer Science, Federal University of Lavras, Lavras, Brazil

    Ricardo M. A. Silva

Authors
  1. Ernesto G. Birgin
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  2. Erico M. Gozzi
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  3. Mauricio G. C. Resende
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  4. Ricardo M. A. Silva
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Corresponding author

Correspondence to Mauricio G. C. Resende.

Additional information

This research was done while Ricardo M. A. Silva was a visiting post-doctoral scholar at AT&T Labs Research. His work was partially funded by Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Brazil.

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Birgin, E.G., Gozzi, E.M., Resende, M.G.C. et al. Continuous GRASP with a local active-set method for bound-constrained global optimization. J Glob Optim 48, 289–310 (2010). https://doi.org/10.1007/s10898-009-9494-z

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  • DOI: https://doi.org/10.1007/s10898-009-9494-z

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