Journal
INVERSE PROBLEMS IN SCIENCE AND ENGINEERING
Volume 16, Issue 6, Pages 717-741Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/17415970802082724
Keywords
hybrid optimization; multidimensional interpolations; response surface; radial basis function polynomials
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In this article, we describe a hybrid optimizer based on a highly accurate response surface method, which uses several radial basis functions and polynomials as interpolants. The response surface is capable to interpolate linear as well as highly non-linear functions in multi-dimensional spaces having up to 500 dimensions. The accuracy, robustness, efficiency, transparency and conceptual simplicity are discussed. Based on the extensive testing performed on 296 test functions, the radial basis functions (RBFs) approach seems computationally easy to implement and results are superior, requiring small computing time. The performance of the RBF approximation is compared with wavelets neural networks for several selected test cases and the optimizer is compared with other hybrid optimizers, as well as with the IOSO commercial code.
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