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Expedited Metaheuristic-Based Antenna Optimization Using EM Model Resolution Management

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Computational Science – ICCS 2023 (ICCS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 10475))

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Abstract

Design of modern antenna systems heavily relies on numerical optimization methods. Their primary purpose is performance improvement by tuning of geometry and material parameters of the antenna under study. For reliability, the process has to be conducted using full-wave electromagnetic (EM) simulation models, which are associated with sizable computational expenditures. The problem is aggravated in the case of global optimization, typically carried out using nature-inspired algorithms. To reduce the CPU cost, population-based routines are often combined with surrogate modeling techniques, frequently in the form of machine learning procedures. While offering certain advantages, their efficiency is worsened by the curse of dimensionality and antenna response nonlinearity. In this article, we investigate computational advantages of combining population-based optimization with variable-resolution EM models. Consequently, a model management scheme is developed, which adjusts the discretization level of the antenna under optimization within the continuous spectrum of acceptable fidelities. Starting from the lowest practically useful fidelity, the resolution converges to the highest assumed level when the search process is close to conclusion. Several adjustment profiles are considered to investigate the speedup-reliability trade-offs. Numerical results have been obtained for two microstrip antennas and particle swarm optimizer as a widely-used nature-inspired algorithm. Consistent acceleration of up to eighty percent has been obtained in comparison to the single-resolution version with minor deterioration of the design quality. Another attractive feature of our methodology is versatility and easy implementation and handling.

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Acknowledgement

The authors would like to thank Dassault Systemes, France, for making CST Microwave Studio available. This work is partially supported by the Icelandic Centre for Research (RANNIS) Grant 206606 and by Gdańsk University of Technology Grant DEC-41/2020/IDUB/I.3.3 under the Argentum Triggering Research Grants program - ‘Excellence Initiative - Research University’.

Author information

Authors and Affiliations

  1. Faculty of Electronics Telecommunications and Informatics, Gdansk University of Technology, Narutowicza 11/12, 80-233, Gdansk, Poland

    Anna Pietrenko-Dabrowska & Slawomir Koziel

  2. Engineering Optimization and Modeling Center, Department of Engineering, Reykjavík University, Menntavegur 1, 102, Reykjavík, Iceland

    Slawomir Koziel

  3. School of Aeronautics and Astronautics, Purdue University, West Lafayette, IN, 47907, USA

    Leifur Leifsson

Authors
  1. Anna Pietrenko-Dabrowska
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  2. Slawomir Koziel
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  3. Leifur Leifsson
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Corresponding author

Correspondence to Slawomir Koziel .

Editor information

Editors and Affiliations

  1. Czech Technical University in Prague, Prague, Czech Republic

    Jiří Mikyška

  2. University of Amsterdam, Amsterdam, The Netherlands

    Clélia de Mulatier

  3. AGH University of Science and Technology, Krakow, Poland

    Maciej Paszynski

  4. University of Amsterdam, Amsterdam, The Netherlands

    Valeria V. Krzhizhanovskaya

  5. University of Tennessee at Knoxville, Knoxville, TN, USA

    Jack J. Dongarra

  6. University of Amsterdam, Amsterdam, The Netherlands

    Peter M.A. Sloot

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Pietrenko-Dabrowska, A., Koziel, S., Leifsson, L. (2023). Expedited Metaheuristic-Based Antenna Optimization Using EM Model Resolution Management. In: Mikyška, J., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M. (eds) Computational Science – ICCS 2023. ICCS 2023. Lecture Notes in Computer Science, vol 10475. Springer, Cham. https://doi.org/10.1007/978-3-031-36024-4_29

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  • DOI: https://doi.org/10.1007/978-3-031-36024-4_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-36023-7

  • Online ISBN: 978-3-031-36024-4

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