Speeding up continuous GRASP

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Speeding up continuous GRASP
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.
Michael J. Hirsch, Panos M. Pardalos, Mauricio G.
Added 10 Dec 2010
Updated 10 Dec 2010
Type Journal
Year 2010
Where EOR
Authors Michael J. Hirsch, Panos M. Pardalos, Mauricio G. C. Resende
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