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Global optimization by continuous GRASP

Michael J. Hirsch (mjh8787***at***ufl.edu)
Claudio N. Meneses (claudio_n_meneses***at***yahoo.com)
Panos M. Pardalos (pardalos***at***ufl.edu)
Mauricio G. C. Resende (mgcr***at***research.att.com)

Abstract: We introduce a novel global optimization method called Continuous GRASP (C-GRASP) which extends Feo and Resende's greedy randomized adaptive search procedure (GRASP) from the domain of discrete optimization to that of continuous global optimization. This stochastic local search method is simple to implement, is widely applicable, and does not make use of derivative information, thus making it a well-suited approach for solving global optimization problems. We illustrate the effectiveness of the procedure on a set of standard test problems as well as two hard global optimization problems.

Keywords: GRASP, global optimization, continuous optimization, stochastic algorithm, stochastic local search, nonlinear programming.

Category 1: Global Optimization (Stochastic Approaches )

Category 2: Combinatorial Optimization (Meta Heuristics )

Citation: AT&T Labs Research Technical Report TD-6MPUV9. AT&T Labs Research, Florham Park, NJ 07932, March 8, 2006.

Download: [PDF]

Entry Submitted: 03/09/2006
Entry Accepted: 03/09/2006
Entry Last Modified: 03/09/2006

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