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GRASP for nonlinear optimization

Claudio N. Meneses (claudio***at***ufl.edu)
Panos M. Pardalos (pardalos***at***ise.ufl.edu)
Mauricio G. C. Resende (mgcr***at***research.att.com)

Abstract: We propose a Greedy Randomized Adaptive Search Procedure (GRASP) for solving continuous global optimization problems subject to box constraints. The method was tested on benchmark functions and the computational results show that our approach was able to find, in a few seconds, optimal solutions for all tested functions despite not using any gradient information about the function being tested. Most metaheuristcs found in the literature have not been capable of finding optimal solutions to the same collection of functions.

Keywords: GRASP, nonlinear programming, global optimization, metaheuristic, heuristic.

Category 1: Global Optimization (Stochastic Approaches )

Category 2: Combinatorial Optimization (Meta Heuristics )

Citation: AT&T Labs Research Technical Report TD-6DUTRG. June 30, 2005.

Download: [PDF]

Entry Submitted: 07/06/2005
Entry Accepted: 07/07/2005
Entry Last Modified: 07/07/2005

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