A Framework for Optimizing Antenna Through Genetic Algorithm-Based Neural Network

Sumeyye Korkmaz; Mohammad Alibakhshikenari; Lida Kouhalvandi

doi:10.2478/amset-2023-0009

Abstract

In this research paper, the application of Artificial Neural Networks (ANN) for designing antennas is introduced. The primary objective of this optimization technique is to focus on the antenna’s parameters to achieve a desirable S₁₁ value, which is a key metric of an antenna’s performance. To demonstrate the effectiveness of this approach, the optimization method to design three different antennas using the Numerical Analyzer (MATLAB) tool is employed. Through this study, it is tried to provide new insights into the optimization of antenna design using ANN and demonstrate its potential applications in the field of antenna engineering. The results of this research could potentially lead to the development of more efficient and effective antenna systems with improved performance in various applications.

References

R. Alhamad, E. Almajali, and S. Mahmoud, “Electrical reconfigurability in modern 4g, 4g/5g and 5g antennas: A critical review of polarization and frequency reconfigurable designs,” IEEE Access, vol. 11, pp. 29 215–29 233, 2023.
Search in Google Scholar Back to article
O. A. Saraereh, “Design and performance evaluation of oam-antennas: A comparative review,” IEEE Access, vol. 11, pp. 27 992–28 013, 2023.
Search in Google Scholar Back to article
D. Tuzi, T. Delamotte, and A. Knopp, “Satellite swarm-based antenna arrays for 6g direct-to-cell connectivity,” IEEE Access, vol. 11, pp. 36 907–36 928, 2023.
Search in Google Scholar Back to article
B. Zhang, L. Song, and Y. Rahmat-Sammi, “Textile patch antenna surrogate-based optimization: Kriging surrogate modeling on equivalent circuit components,” in 2023 United States National Committee of URSI National Radio Science Meeting (USNC-URSI NRSM), 2023, pp. 145–146.
Search in Google Scholar Back to article
P.-F. Qin, W.-H. Li, D. Wang, G.-L. Huang, W. Fan, and C.-Y.-D. Sim, “A linear decreasing inertia weight particle swarm optimization base on a k-means clustering chaotic sampling for antenna design,” in 2022 Cross Strait Radio Science Wireless Technology Conference (CSRSWTC), 2022, pp. 1–3.
Search in Google Scholar Back to article
A. A. Al-Azza, A. A. Al-Jodah, and F. J. Harackiewicz, “Spider monkey optimization: A novel technique for antenna optimization,” IEEE Antennas and Wireless Propagation Letters, vol. 15, pp. 1016–1019, 2016.
Search in Google Scholar Back to article
F. Mir, L. Kouhalvandi, L. Matekovits, and E. O. Gunes, “Automated optimization for broadband flat-gain antenna designs with artificial neural network,” IET Microwaves, Antennas & Propagation, vol. 15, no. 12, pp. 1537–1544, 2021. [Online]. Available: https://ietresearch.onlinelibrary.wiley.com/doi/abs/10.1049/mia2.12137
Search in Google Scholar Back to article
F. Mir, L. Kouhalvandi, and L. Matekovits, “Deep neural learning based optimization for automated high performance antenna designs,” Scientific Reports, vol. 12, no. 16801, 2022.
Search in Google Scholar Back to article
F. Shuai, “Design system of plant decorative ceramic pattern based on multi-objective genetic algorithm,” in 2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS), 2023, pp. 1–6.
Search in Google Scholar Back to article

A Framework for Optimizing Antenna Through Genetic Algorithm-Based Neural Network

Abstract

Paradigm

My account