Ensemble of surrogates

Goel, Tushar; Haftka, Raphael T.; Shyy, Wei; Queipo, Néstor V.

doi:10.1007/s00158-006-0051-9

Cited by 571 publications

(265 citation statements)

References 13 publications

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“…When the low-order polynomials are used for global metamodels in weakly nonlinear simulation to approximate its global tendency, the Latin hypercube sampling (LHS) technique is one of the most popular choices both in scientific research and engineering problems [21,31,33,34]. LHS maximizes the minimum distance between the sampling points to obtain uniform designs; moreover its projections onto each variable axis give uniform points.…”

Section: Numerical Procedurementioning

confidence: 99%

Novel Two-Stage Method for Low-Order Polynomial Model

Cheng

Shen

Guo

2018

Mathematical Problems in Engineering

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One of the most popular statistical models is a low-order polynomial response surface model, i.e., a polynomial of first order or second order. These polynomials can be used for global metamodels in weakly nonlinear simulation to approximate their global tendency and local metamodels in response surface methodology (RSM), which has been studied in various applications in engineering design and analysis. The order of the selected polynomial determines the number of sampling points (input combinations) and the resulting accuracy (validity, adequacy). This paper derives a novel method to obtain an accurate high-order polynomial while requiring fewer sampling points. This method uses a two-stage procedure such that the second stage modifies the low-order polynomial estimated in the first stage; this second stage does not require new points. This paper evaluates the performance of the method numerically by using several test functions. These numerical results show that the proposed method can provide more accurate predictions than the traditional method.

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Section: Numerical Procedurementioning

confidence: 99%

Novel Two-Stage Method for Low-Order Polynomial Model

Cheng

Shen

Guo

2018

Mathematical Problems in Engineering

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“…Besides individual surrogates it is common also to consider a weighted combination of surrogates (e.g., Goel et al 2007). Müller and Piché (2011) introduced one such combination based on Dempster-Shafer theory, and Müller and Shoemaker (2014) parallelized it by adding random sampling similar to that of Regis and Shoemaker (2007b).…”

Section: Multiple Surrogatesmentioning

confidence: 99%

Parallel surrogate-assisted global optimization with expensive functions – a survey

Haftka

Villanueva

Chaudhuri

2016

Struct Multidisc Optim

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204

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Surrogate assisted global optimization is gaining popularity. Similarly, modern advances in computing power increasingly rely on parallelization rather than faster processors. This paper examines some of the methods used to take advantage of parallelization in surrogate based global optimization. A key issue focused on in this review is how different algorithms balance exploration and exploitation. Most of the papers surveyed are adaptive samplers that employ Gaussian Process or Kriging surrogates. These allow sophisticated approaches for balancing exploration and exploitation and even allow to develop algorithms with calculable rate of convergence as function of the number of parallel processors. In addition to optimization based on adaptive sampling, surrogate assisted parallel evolutionary algorithms are also surveyed. Beyond a review of the present state of the art, the paper also argues that methods that provide easy parallelization, like multiple parallel runs, or methods that rely on population of designs for diversity deserve more attention.

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“…Cross validation method [15] is used in this study. The main idea of this method is to divide the data into K subsets (K-fold cross validation) of approximately equal size.…”

Section: (C) Radial Based Neural Network (Rbnn)mentioning

confidence: 99%

Global design optimization for an axial-flow tandem pump based on surrogate method

Li¹,

Zhao²,

Wang³

2013

IOP Conf. Ser.: Mater. Sci. Eng.

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Abstract. Tandem pump, compared with multistage pump, goes without guide vanes between impellers. Better cavitation performance and significant reduction of the axial geometry scale is important for high-speed propulsion. This study presents a global design optimization method based on surrogated method for an axial-flow tandem pump to enhance trade-off performances: energy and cavitation performances. At the same time, interactions between impellers and impacts on the performances are analyzed. Fixed angle of blades in impellers and phase angle are performed as design variables. Efficiency and minimum average pressure coefficient (MAPC) on axial sectional surface in front impeller are the objective function, which can represent energy and cavitation performances well. Different surrogate models are constructed, and Global Sensitivity Analysis and Pareto Front method are used. The results show that, 1) Influence from phase angle on performances can be neglected compared with other two design variables, 2) Impact ratio of fixed angle of blades in two impellers on efficiency are the same as their designed loading distributions, which is 4:6, 3) The optimization results can enhance the trade-off performances well: efficiency is improved by 0.6%, and the MAPC is improved by 4.5%.

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Ensemble of surrogates

Cited by 571 publications

References 13 publications

Novel Two-Stage Method for Low-Order Polynomial Model

Novel Two-Stage Method for Low-Order Polynomial Model

Parallel surrogate-assisted global optimization with expensive functions – a survey

Global design optimization for an axial-flow tandem pump based on surrogate method

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