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

Ernesto G. Birgin (egbirgin***at***ime.usp.br)
Erico M. Gozzi (erico.gozzi***at***gmail.com)
Mauricio G. C. Resende (mgcr***at***research.att.com)
Ricardo M. A. Silva (rmas***at***dcc.ufla.br)

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.

Keywords: Global optimization, stochastic methods, active-set methods, heuristic, CGRASP, GENCAN

Category 1: Global Optimization

Category 2: Global Optimization (Stochastic Approaches )

Citation: AT&T Labs Research Technical Report, AT&T Labs Research, Shannon Laboratory, Florham Park, NJ 07932, April 2009.

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Entry Submitted: 04/27/2009
Entry Accepted: 05/02/2009
Entry Last Modified: 05/03/2009

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