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Speeding up continuous GRASP
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DOI：10.1016/j.ejor.2010.02.009.png)
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Abstract
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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. (C) 2010 Elsevier BM. All rights reserved.
Keywords:
GRASP
Continuous GRASP
Global optimization
Multimodal functions
Continuous optimization
Heuristic
Stochastic algorithm
Stochastic local search
Nonlinear programming
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