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2007

Parallel radial basis function methods for the global optimization of expensive functions

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Parallel radial basis function methods for the global optimization of expensive functions
We introduce a master–worker framework for parallel global optimization of computationally expensive functions using response surface models. In particular, we parallelize two radial basis function (RBF) methods for global optimization, namely, the RBF method by Gutmann [Gutmann, H.M., 2001a. A radial basis function method for global optimization. Journal of Global Optimization 19(3), 201–227] (Gutmann-RBF) and the RBF method by Regis and Shoemaker [Regis, R.G., Shoemaker, C.A., 2005. Constrained global optimization of expensive black box functions using radial basis functions, Journal of Global Optimization 31, 153–171] (CORS-RBF). We modify these algorithms so that they can generate multiple points for simultaneous evaluation in parallel. We compare the performance of the two parallel RBF methods with a parallel multistart derivative-based algorithm, a parallel multistart derivative-free trust-region algorithm, and a parallel evolutionary algorithm on eleven test problems and ...
Rommel G. Regis, Christine A. Shoemaker
Added 13 Dec 2010
Updated 13 Dec 2010
Type Journal
Year 2007
Where EOR
Authors Rommel G. Regis, Christine A. Shoemaker
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