 Speeding up continuous GRASPM.J. Hirsch, P.M. Pardalos and M.G.C. Resende European Journal of Operational Research, 2010, vol. 205, issue 3, 507-521 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., 2007). 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 (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:205:y:2010:i:3:p:507-521 Access Statistics for this article European Journal of Operational Research is currently edited by Roman Slowinski, Jesus Artalejo, Jean-Charles. Billaut, Robert Dyson and Lorenzo Peccati More articles in European Journal of Operational Research from Elsevier |
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