Comparison of response surface and kriging models for multidisciplinary design optimization

Simpson, Timothy W.; Korte, John J.; Mauery, Timothy M.; Mistree, Farrokh

doi:10.2514/6.1998-4755

Cited by 372 publications

(193 citation statements)

References 23 publications

Supporting

Mentioning

178

Contrasting

Unclassified

Order By: Relevance

“…The F(x) creates localized deviation at some unknown point x using Gaussian correlation with variance (σ 2 ) (Simpson, Mistree, Korte, & Mauery, 1998).…”

Section: Kriging Model (Krg)mentioning

confidence: 99%

Shape optimization of a bidirectional impulse turbine via surrogate models

Ezhilsabareesh

Rhee

Samad

2017

Engineering Applications of Computational Fluid Mechanics

View full text Add to dashboard Cite

In this work, a bidirectional impulse turbine used in a wave energy device is simulated using a CFD technique and a shape optimization performed with multiple surrogates-assisted multi-objective evolutionary algorithm. The surrogates have been generated using the validated CFD technique. The objectives for the optimization are to maximize the shaft power and to minimize the pressure drop across the turbine. The hub and the tip thickness of the rotor blade profiles are modified and considered as the design variables. The Latin hypercube sampling technique generated design points are evaluated in the CFD solver, the objective responses are used to construct multiple surrogates, and a Pareto optimal front is generated through a multi-objective optimization approach. The turbine efficiency, which is the function of pressure drop and shaft power, is relatively increased by 10.4%.

show abstract

“…The F(x) creates localized deviation at some unknown point x using Gaussian correlation with variance (σ 2 ) (Simpson, Mistree, Korte, & Mauery, 1998).…”

Section: Kriging Model (Krg)mentioning

confidence: 99%

Shape optimization of a bidirectional impulse turbine via surrogate models

Ezhilsabareesh

Rhee

Samad

2017

Engineering Applications of Computational Fluid Mechanics

View full text Add to dashboard Cite

show abstract

“…However, one cannot find an agreement on the dominance of one specific method over others. In an early study, Simpson et al (1998) compared secondorder response surfaces with Kriging. The metamodels were applied on a multidisciplinary design problem and four optimization problems.…”

Section: Introductionmentioning

confidence: 99%

Radial basis functions as surrogate models with a priori bias in comparison with a posteriori bias

Amouzgar

Strömberg

2016

Struct Multidisc Optim

View full text Add to dashboard Cite

In order to obtain a robust performance, the established approach when using radial basis function networks (RBF) as metamodels is to add a posteriori bias which is defined by extra orthogonality constraints. We mean that this is not needed, instead the bias can simply be set a priori by using the normal equation, i.e. the bias becomes the corresponding regression model. In this paper we demonstrate that the performance of our suggested approach with a priori bias is in general as good as, or even for many test examples better than, the performance of RBF with a posteriori bias. Using our approach, it is clear that the global response is modelled with the bias and that the details are captured with radial basis functions. The accuracy of the two approaches are investigated by using multiple test functions with different degrees of dimensionality. Furthermore, several modeling criteria, such as the type of radial basis functions used in the RBFs, dimension of the test functions, sampling techniques and size of samples, are considered to study their affect on the performance of the approaches. surrogate based design optimization is also demonstrated by solving an established engineering benchmark of a welded beam and another benchmark for different sampling sets generated by successive screening, random, Latin hypercube and Hammersley sampling, respectively. The results obtained by evaluation of the performance metrics, the modeling criteria and the presented optimal solutions, demonstrate promising potentials of our RBF with a priori bias, in addition to the simplicity and straight-forward use of the approach.

show abstract

“…Some of the popular approximation models in evolutionary computation are quadratic models [7], kriging models [7,8], neural network models [9] and radial basis function (RBF) network models [10][11][12][13]. In these approximation strategies, objective function is estimated by approximation model and the optimization problem is solved utilizing the approximated values.…”

Section: Introductionmentioning

confidence: 99%

Reduction of function evaluation in differential evolution using nearest neighbor comparison

Pham

2014

Vietnam J Comput Sci

View full text Add to dashboard Cite

Approximation models have recently been introduced to differential evolution (DE) to reduce expensive fitness evaluation in function optimization. These models basically require additional control parameters and/or external storage for the learning process. Depending on the choice of the additional parameters, the strategies may have different levels of efficiency. The present paper introduces an alternative way for reducing function evaluations in differential evolution, which does not require additional control parameter and external archive. The algorithm uses a nearest neighbor in the search population to judge whether a new point is worth evaluating, so that unnecessary evaluations can be avoided. The performance of this new scheme of differential evolution, known as differential evolution with nearest neighbor comparison (DE-NNC), is demonstrated and compared with that of standard DE as well as approximation models including differential evolution using k-nearest neighbor predictor (DE-kNN), differential evolution using speeded-up k-nearest neighbor estimator (DE-EkNN)

show abstract

Comparison of response surface and kriging models for multidisciplinary design optimization

Cited by 372 publications

References 23 publications

Shape optimization of a bidirectional impulse turbine via surrogate models

Shape optimization of a bidirectional impulse turbine via surrogate models

Radial basis functions as surrogate models with a priori bias in comparison with a posteriori bias

Reduction of function evaluation in differential evolution using nearest neighbor comparison

Contact Info

Product

Resources

About