A Critical Review of Surrogate Assisted Robust Design Optimization

Chatterjee, Tanmoy; Chakraborty, Souvik; Chowdhury, Rajib

doi:10.1007/s11831-017-9240-5

Cited by 143 publications

(48 citation statements)

References 107 publications

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“…Benková et al (2015) discusses space-…lling designs that may satisfy various criteria and input constraints such that the input space is not a k-dimensional cube, including so-called bridge designs. Chen et al (2016) shows that "there is substantial variation in prediction accuracy over equivalent designs".…”

Section: Ordinary Krigingmentioning

confidence: 99%

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Kriging: Methods and Applications

Kleijnen

2017

SSRN Journal

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In this chapter we present Kriging-also known as a Gaussian process (GP) model-which is a mathematical interpolation method. To select the input combinations to be simulated, we use Latin hypercube sampling (LHS); we allow uniform and non-uniform distributions of the simulation inputs. Besides deterministic simulation we discuss random simulation, which requires adjusting the design and analysis. We discuss sensitivity analysis of simulation models, using "functional analysis of variance" (FANOVA)-also known as Sobol sensitivity indexes. Finally, we discuss optimization of the simulated system, including "robust" optimization.

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Section: Ordinary Krigingmentioning

confidence: 99%

“…Kriging for RO is also used in Chatterjee et al (2017), comparing this approach with several alternative metamodel types (e.g., neural networks).…”

Section: Robust Optimization (Ro)mentioning

confidence: 99%

Kriging: Methods and Applications

Kleijnen

2017

SSRN Journal

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“…Recently, a strategy for reducing the running time has presented by Sayyafzadeh [19] that is based on a self-adaptive metamodeling approach. Also, a number of metamodeling strategies that could be used for the uncertainty-based design optimization have reviewed by Chatterjee et al [20].…”

Section: Introductionmentioning

confidence: 99%

Computational Intelligence and Its Applications in Uncertainty-Based Design Optimization

Bataleblu¹

2020

Bridge Optimization - Inspection and Condition Monitoring

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The large computational cost, the curse of dimensionality and the multidisciplinary nature are known as the main challenges in dealing with real-world engineering optimization problems. The consideration of inevitable uncertainties in such problems will exacerbate mentioned difficulties as much as possible. Therefore, the computational intelligence methods (also known as surrogate-models or metamodels, which are computationally cheaper approximations of the true expensive function) have been considered as powerful paradigms to overcome or at least to alleviate the mentioned issues over the last three decades. This chapter presents an extensive survey on surrogate-assisted optimization (SAO) methods. The main focus areas are the working styles of surrogate-models and the management of the metamodels during the optimization process. In addition, challenges and future trends of this field of study are introduced. Then, a comparison study will be carried out by employing a novel evolution control strategies (ECS) and recently developed efficient global optimization (EGO) method in the framework of uncertainty-based design optimization (UDO). To conclude, some open research questions in this area are discussed.

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“…Kriging has many advantages; such as being a proper approach to multi-dimensional problems and its prediction capability as compared to other metamodeling techniques [31][32] and being able to produce error estimates. On the other hand, Kriging technique is still developing by many research groups from all around the world [33][34][35][36][67][68]. Those development studies are rather concentrate on the sampling types [37], on the tuning parameter exploration such as variogram adaptation [38] or on adding some descriptive new information into the algorithm such as gradient/hessian enhanced types [39][40][41][42][43].…”

Section: Introductionmentioning

confidence: 99%

Multi-Dimensional Surrogate Based Aft Form Optimization of Ships Using High Fidelity Solvers

Solak

2020

brod

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Surrogate (metamodel) based optimization has numerous potential applications in the field of naval architecture. It is aimed here to establish a methodology for the aft form optimization for minimum viscous resistance, thus the present study is focused on the aft form where the viscous effects become dominant. It is necessary to solve this problem within acceptable time span from practical naval architectural point of view which requires metamodeling techniques currently under investigation. Accordingly, the present paper investigates the metamodeling ability of the Kriging interpolation and attempts to explore its capabilities and limitations in the aft form optimization from viscous resistance point of view. As metamodeling techniques become more widely used, their constraints are more apparent. Especially in highly nonlinear design spaces, the effect of dimensionality should be taken into consideration. Taking all those factors into account, the present paper is to examine the capabilities of Kriging and to establish the learning performance in terms of RMS error, correlation coefficient and required number of training points according to selected optimization algorithm for multidimensional ship design problem. The results show that, at least 5% reduction in viscous pressure drag can be attained by the present optimization methodology.

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A Critical Review of Surrogate Assisted Robust Design Optimization

Cited by 143 publications

References 107 publications

Kriging: Methods and Applications

Kriging: Methods and Applications

Computational Intelligence and Its Applications in Uncertainty-Based Design Optimization

Multi-Dimensional Surrogate Based Aft Form Optimization of Ships Using High Fidelity Solvers

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