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.

• Type: Journal
• Year: 2010
• Where: EOR
• Authors: Michael J. Hirsch, Panos M. Pardalos, Mauricio G. C. Resende
• Keywords: Continuous Global Optimization, EOR 2010, Greedy Randomized Adaptive Search Procedure, Greedy Randomized Fashion