Surrogate-Based Design Optimization of Multi-Band Antenna

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Belen A., Tari O., MAHOUTİ P., Belen M. A., ÇALIŞKAN A.

Applied Computational Electromagnetics Society Journal, vol.37, no.1, pp.34-40, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 37 Issue: 1
  • Publication Date: 2022
  • Doi Number: 10.13052/2022.aces.j.370104
  • Journal Name: Applied Computational Electromagnetics Society Journal
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Compendex, DIALNET
  • Page Numbers: pp.34-40
  • Keywords: Artificial Neural Network (ANN), multiband antenna, optimization, surrogate modeling
  • Yıldız Technical University Affiliated: Yes


© 2022 Applied Computational Electromagnetics Society (ACES). All rights reserved.In this work, design optimization process of a multi-band antenna via the use of artificial neural network (ANN) based surrogate model and meta-heuristic optimizers are studied. For this mean, first, by using Latin-Hyper cube sampling method, a data set based on 3D full wave electromagnetic (EM) simulator is generated to train an ANN-based model. By using the ANNbased surrogate model and a meta-heuristic optimizer invasive weed optimization (IWO), design optimization of a multi-band antenna for (1) 2.4-3.6 GHz for ISM, LTE, and 5G sub-frequencies, and (2) 9-10 GHz for X-band applications is aimed. The obtained results are compared with the measured and simulated results of 3D EM simulation tool. Results show that the proposed methodology provides a computationally efficient design optimization process for design optimization of multiband antennas.