Efficient Global Optimization of Expensive Black-Box Functions
Journal of Global Optimization
Comparison of Response Surface and Kriging Models in the Multidisciplinary Design of an Aerospike Nozzle
A Comparison of Approximation Modeling Techniques: Polynomial Versus Interpolating Models
A Comparison of Approximation Modeling Techniques: Polynomial Versus Interpolating Models
Finite Elements in Analysis and Design
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To prevent the dogboning effect of stent implantation (i.e. the ends of a stent opening first during expansion), an adaptive optimization method based on the kriging surrogate model is proposed to reduce the absolute value of the dogboning rate. Integrating design of experiment (DOE) methods with the kriging surrogate model can approximate the functional relationship between the dogboning rate and the geometrical design parameters of the stent, replacing the expensive reanalysis of the stent dogboning rate during the optimization process. In this adaptive process, an infilling sampling criterion termed expected improvement (EI) is used to balance local and global search and tends to find the global optimal design. Finite element method is used to analyze stent expansion. As an example, a typical diamond-shaped coronary stent is investigated, where four key geometries are selected to be the design variables. Numerical results demonstrate that the proposed adaptive optimization method can effectively decrease the absolute value of the dogboning rate of stent dilation.