Multi-Fidelity Local Surrogate Model for Computationally Efficient Microwave Component Design Optimization - Publikacja - MOST Wiedzy

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Multi-Fidelity Local Surrogate Model for Computationally Efficient Microwave Component Design Optimization

Abstrakt

In order to minimize the number of evaluations of high-fidelity (“fine”) model in the optimization process, to increase the optimization speed, and to improve optimal solution accuracy, a robust and computational-efficient multi-fidelity local surrogate-model optimization method is proposed. Based on the principle of response surface approximation, the proposed method exploits the multi-fidelity coarse models and polynomial interpolation to construct a series of local surrogate models. In the optimization process, local region modeling and optimization are performed iteratively. A judgment factor is introduced to provide information for local region size update. The last local surrogate model is refined by space mapping techniques to obtain the optimal design with high accuracy. The operation and efficiency of the approach are demonstrated through design of a bandpass filter and a compact ultra-wide-band (UWB) multiple-in multiple-out (MIMO) antenna. The response of the optimized design of the fine model meet the design specification. The proposed method not only has better convergence compared to an existing local surrogate method, but also reduces the computational cost substantially

Cytowania

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Informacje szczegółowe

Kategoria:
Publikacja w czasopiśmie
Typ:
artykuł w czasopiśmie wyróżnionym w JCR
Opublikowano w:
SENSORS nr 19, strony 1 - 13,
ISSN: 1424-8220
Język:
angielski
Rok wydania:
2019
Opis bibliograficzny:
Song Y., Cheng Q., Kozieł S.: Multi-Fidelity Local Surrogate Model for Computationally Efficient Microwave Component Design Optimization// SENSORS. -Vol. 19, iss. 13 (2019), s.1-13
DOI:
Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.3390/s19133023
Bibliografia: test
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Weryfikacja:
Politechnika Gdańska

wyświetlono 28 razy

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