Speeding up continuous GRASP

M.J. Hirsch, P. M. Pardalos, and M. G. C. Resende

European J. on Operational Research, vol. 205, pp. 507-521, 2010.

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.


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