Speeding up continuous GRASP

Michael J. Hirsch (mjh8787ufl.edu)
Panos M. Pardalos (pardalosufl.edu)
Mauricio G. C. Resende (mgcrresearch.att.com)

Abstract: Continuous GRASP (C-GRASP) is a stochastic local search metaheuristic for finding cost-efficient solutions to continuous global optimization problems subject to box constraints (Hirsch et al., 2006). Like a greedy randomized adaptive search procedure (GRASP), a C-GRASP is a multi-start procedure where a starting solution for local improvement is constructed in a greedy randomized fashion. In this paper, we describe several improvements that speed up the original C-GRASP and make it more robust. We compare the new C-GRASP with the original version as well as with other algorithms from the recent literature on a set of benchmark multimodal test functions whose global minima are known. Hart's sequential stopping rule (1998) is implemented and C-GRASP is shown to converge on all test problems.

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

Category 1: Global Optimization

Category 2: Global Optimization (Stochastic Approaches )

Category 3: Combinatorial Optimization (Meta Heuristics )

Citation: AT&T Labs Research Technical Report TD-6U2P2H, AT&T Shannon Laboratory, 180 Park Avenue, Florham Park, NJ 07932 USA, September 2006.

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Entry Submitted: 09/27/2006
Entry Accepted: 09/28/2006
Entry Last Modified: 09/28/2006

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