Rapid Surrogate-Aided Multi-Criterial Optimization of Compact Microwave Passives Employing Machine Learning and ANNs - Publikacja - MOST Wiedzy

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Rapid Surrogate-Aided Multi-Criterial Optimization of Compact Microwave Passives Employing Machine Learning and ANNs

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This article introduces an innovative method for achieving low-cost and reliable multi-objective optimization (MO) of microwave passive circuits. The technique capitalizes on the attributes of surrogate models, specifically artificial neural networks (ANNs), and multi-resolution electromagnetic (EM) analysis. We integrate the search process into a machine learning (ML) framework, where each iteration produces multiple infill points selected from the present representation of the Pareto set. This collection is formed by optimizing the ANN metamodel by means of a multi-objective evolutionary algorithm. The procedure concludes upon convergence, defined as a significant similarity between the sets of non-dominated solutions acquired through consecutive iterations. Performing the majority of iterations at the low-fidelity EM simulation level enables additional computational savings. Our methodology has been showcased using two microstrip circuits. Comparative assessments against various surrogate-assisted benchmark methods demonstrate the algorithm's competitive performance in terms of computational efficiency and the quality of the Pareto set generated in the course of the optimization run.

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Kategoria:
Publikacja w czasopiśmie
Typ:
artykuły w czasopismach
Opublikowano w:
IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES nr 72, strony 4475 - 4488,
ISSN: 0018-9480
Język:
angielski
Rok wydania:
2024
Opis bibliograficzny:
Kozieł S., Pietrenko-Dąbrowska A.: Rapid Surrogate-Aided Multi-Criterial Optimization of Compact Microwave Passives Employing Machine Learning and ANNs// IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES -Vol. 72,iss. 8 (2024), s.4475-4488
DOI:
Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.1109/tmtt.2024.3359703
Źródła finansowania:
  • Publikacja bezkosztowa
Weryfikacja:
Politechnika Gdańska

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