Journal of Global Optimization

, Volume 48, Issue 2, pp 289–310 | Cite as

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

  • Ernesto G. Birgin
  • Erico M. Gozzi
  • Mauricio G. C. Resende
  • Ricardo M. A. Silva


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.


Global optimization Stochastic methods Active-set methods Heuristic CGRASP GENCAN 


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Copyright information

© AT&T Intellectual Property 2009

Authors and Affiliations

  • Ernesto G. Birgin
    • 1
  • Erico M. Gozzi
    • 1
  • Mauricio G. C. Resende
    • 2
  • Ricardo M. A. Silva
    • 3
  1. 1.Instituto de Matemática e EstatísticaUniversidade de São PauloSão PauloBrazil
  2. 2.Algorithms and Optimization Research DepartmentAT&T Labs ResearchFlorham ParkUSA
  3. 3.Department of Computer ScienceFederal University of LavrasLavrasBrazil

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