CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
An adaptive hybrid surrogate model
Structural and Multidisciplinary Optimization
An evolution strategy assisted by an ensemble of local Gaussian process models
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Design optimization of balloon-expandable coronary stent
Structural and Multidisciplinary Optimization
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Two methods of creating approximation models are compared through the calculation of the modeling accuracy on test problems involving one, five, and ten independent variables. Here, the test problems are representative of the modeling challenges typically encountered in realistic engineering optimization problems. The first approximation model is a quadratic polynomial created using the method of least squares. This type of polynomial model has seen considerable use in recent engineering optimization studies due to its computational simplicity and ease of use. However, quadratic polynomial models may be of limited accuracy when the response data to be modeled have multiple local extrema. The second approximation model employs an interpolation scheme known as kriging developed in the fields of spatial statistics and geostatistics. This class of interpolating model has the flexibility to model response data with multiple local extrema. However, this flexibility is obtained at an increase in computational expense and a decrease in ease of use. The intent of this study is to provide an initial exploration of the accuracy and modeling capabilities of these two approximation methods.