2019
DOI: 10.1111/coin.12237
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An approximate optimization strategy using refined hybrid metamodel

Abstract: An approximate model called metamodel or surrogate model is a mathematical model that numerically approximates response of a system during an engineering simulation process or test. The introduction of a metamodel makes it possible to express response defined in the design problem as a simple mathematical function of design variables. A metamodel can be built with response surface method (RSM), kriging, neural network, radial basis function, and so on. Each method has its advantages and disadvantages. A combin… Show more

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Cited by 5 publications
(6 citation statements)
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“…In this study, y(x) can be weight, first natural frequency, or fatigue life. [13][14][15][16][17] If d y(x) is designated as the approximation model, and the mean squared errors of y(x) and d y(x) are minimized to satisfy the unbiased condition, y(x) can be estimated as…”
Section: Kriging Interpolation Methods and Resultsmentioning
confidence: 99%
See 3 more Smart Citations
“…In this study, y(x) can be weight, first natural frequency, or fatigue life. [13][14][15][16][17] If d y(x) is designated as the approximation model, and the mean squared errors of y(x) and d y(x) are minimized to satisfy the unbiased condition, y(x) can be estimated as…”
Section: Kriging Interpolation Methods and Resultsmentioning
confidence: 99%
“…Kriging is an interpolation method named after a South African mining engineer named D. G. Krige, who developed the technique while increasing the accuracy in predicting ore reserves. [13][14][15][16][17] Kriging interpolation for an approximate model is well documented in the references. In general, a response function, y(x), is represented as…”
Section: Kriging Interpolation Methods and Resultsmentioning
confidence: 99%
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“…35 ANOVA method is used to simplify the model keeping only the significant variables, with a significance value of 0.05. 36,37 3. The surrogate models for each objective function together with the operation constraints (4), (5), and (6) form the multi-objective optimization model for the problem.…”
Section: Solution Methodsmentioning
confidence: 99%