Surrogate modeling and optimization of inductor performances using Kriging functions

Passos, Fábio; Gonzalez-Echevarria, R.; Roca, E.; Castro-López, R.; Fernandez, F.V.

doi:10.1109/smacd.2015.7301675

Cited by 4 publications

(3 citation statements)

References 16 publications

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“…Lots of surrogate-model based optimization algorithms were implemented in genetic algorithm (GA) literature (Glaz et al , 2009). Besides them, there are similar studies from PSO literature (Praveen and Duvigneau, 2009; Singh and Grandhi, 2010; Carrese et al , 2011; Yuanfu et al , 2013, Fang et al , 2014; Passos et al , 2015; Mehmani et al , 2015; Tan et al , 2015). In addition to a conventional surrogate modeling approach, another methodology called a hybrid approach was also used in GA and PSO.…”

Section: Surrogate Modelingmentioning

confidence: 80%

Double surrogate modeling usage in PSO

Pehlivanoglu¹

2017

AEAT

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Purpose The purpose of this paper is to improve the efficiency of particle optimization method by using direct and indirect surrogate modeling in inverse design problems. Design/methodology/approach The new algorithm emphasizes the use of a direct and an indirect design prediction based on local surrogate models in particle swarm optimization (PSO) algorithm. Local response surface approximations are constructed by using radial basis neural networks. The principal role of surrogate models is to answer the question of which individuals should be placed into the next swarm. Therefore, the main purpose of surrogate models is to predict new design points instead of estimating the objective function values. To demonstrate its merits, the new approach and six comparative algorithms were applied to two different test cases including surface fitting of a geographical terrain and an inverse design of a wing, the averaged best-individual fitness values of the algorithms were recorded for a fair comparison. Findings The new algorithm provides more than 60 per cent reduction in the required generations as compared with comparative algorithms. Research limitations/implications The comparative study was carried out only for two different test cases. It is possible to extend test cases for different problems. Practical implications The proposed algorithm can be applied to different inverse design problems. Originality/value The study presents extra ordinary application of double surrogate modeling usage in PSO for inverse design problems.

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Section: Surrogate Modelingmentioning

confidence: 80%

Double surrogate modeling usage in PSO

Pehlivanoglu¹

2017

AEAT

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“…A variety of correlation functions are available in literature to fit a Kriging model (Couckuyt et al 2012;Fang et al 2005;Kaymaz 2005;Kersaudy et al 2015;Kleijnen 2009;Kleijnen and van Beers 2005;Moyeed and Papritz 2002;Passos et al 2015;Picheny et al 2013a;Ulaganathan et al 2014Ulaganathan et al , 2015Wang and Shan 2005). For this study, a selection of commonly used correlation functions, i.e.…”

Section: Krigingmentioning

confidence: 99%

“…For this study, a selection of commonly used correlation functions, i.e. Gaussian (de Oliveira et al 2013;Kleijnen 2009;Passos et al 2015;Picheny et al 2010), Matérn 3/2 (Picheny et al 2013a;Ulaganathan et al 2014Ulaganathan et al , 2015 and Matérn 5/2 (Ulaganathan et al 2014(Ulaganathan et al , 2015, is considered for the study of the performance of the Kriging metamodels. The common choice for the functional form for the global function f (see (3)) which gives the overall trend is a polynomial (linear or quadratic).…”

Section: Krigingmentioning

confidence: 99%

Performance evaluation of metamodelling methods for engineering problems: towards a practitioner guide

Kianifar

Campean

2019

Struct Multidisc Optim

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Metamodelling or surrogate modelling techniques are frequently used across the engineering disciplines in conjunction with expensive simulation models or physical experiments. With the proliferation of metamodeling techniques developed to provide enhanced performance for specific problems, and the wide availability of a diverse choice of tools in engineering software packages, the engineering task of selecting a robust metamodeling technique for practical problems is still a challenge. This research introduces a framework for describing the typology of engineering problems, in terms of dimensionality and complexity, and the modelling conditions, reflecting the noisiness of the signals and the affordability of sample sizes, and on this basis presents a systematic evaluation of the performance of frequently used metamodeling techniques. A set of metamodeling techniques, selected based on their reported use for engineering problems (i.e. Polynomial, Radial Basis Function, and Kriging), were systematically evaluated in terms of accuracy and robustness against a carefully assembled set of 18 test functions covering different types of problems, sampling conditions and noise conditions. A set of four real-world engineering case studies covering both computer simulation and physical experiments were also analysed as validation tests for the proposed guidelines. The main conclusions drawn from the study are that Kriging model with Matérn 5/2 correlation function performs consistently well across different problem types with smooth (i.e. not noisy) data, while Kriging model with Matérn 3/2 correlation function provides robust performance under noisy conditions, except for the very high noise conditions, where the Kriging model with nugget appears to provide better models. These results provide engineering practitioners with a guide for the choice of a metamodeling technique for problem types and modelling conditions represented in the study, whereas the evaluation framework and benchmarking problems set will be useful for researchers conducting similar studies. Structural and Multidisciplinary Optimization (2020) 61:159-186 Performance evaluation of metamodelling methods for engineering problems: towards a practitioner guide 161

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An efficient optimization method of RF passive components using RBF model

Yin

Zhang

et al. 2021

2021 IEEE 14th International Conference on ASIC (ASICON)

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Surrogate modeling and optimization of inductor performances using Kriging functions

Cited by 4 publications

References 16 publications

Double surrogate modeling usage in PSO

Double surrogate modeling usage in PSO

Performance evaluation of metamodelling methods for engineering problems: towards a practitioner guide

An efficient optimization method of RF passive components using RBF model

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