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Journals / Karaelmas Fen ve Mühendislik Dergisi / 2026 / Cilt: 16 Sayı: 1

Enhanced Prediction of Gain and Bandwidth for Metamaterial Antenna Using Machine Learning and Metaheuristic Optimization

Metamalzeme Anten için Sezgisel Optimizasyon ve Makine Öğrenmesi Kullanılarak Kazanç ve Bant Genişliğinin Gelişmiş Tahmini

Nesibe Yalçın(ERCIYES UNIVERSITY)0000-0003-0324-9111
Sibel Ünaldı(BILECIK SEYH EDEBALI UNIVERSITY)0000-0001-9948-4284
Semih Çakır(BULENT ECEVIT UNIVERSITY)0000-0003-3072-9532
Pages
49–56
Publication Date
2026-04-21
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The optimized ML models can be used to predict antenna gain and bandwidth more accurately.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The ongoing demand for improved performance in modern communication systems has driven recent developments in electromagnetic component design.
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Key Findings & Data:Also, in the antenna design process, both the achievement of multiple performances (such as size, gain, bandwidth, and return loss) and time efficiency have become increasingly important, and this is accomplished through advanced techniques like Machine Learning (ML) integrated with optimization algorithms.
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Conclusion & Impact:The optimized ML models can be used to predict antenna gain and bandwidth more accurately.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Bant genişliği · hiperparametre ayarı · makine öğrenmesi

Abstract

The ongoing demand for improved performance in modern communication systems has driven recent developments in electromagnetic component design. Integrating metamaterial unit cell structures into antenna designs can be a solution for improving electromagnetic radiation properties of antennas. Also, in the antenna design process, both the achievement of multiple performances (such as size, gain, bandwidth, and return loss) and time efficiency have become increasingly important, and this is accomplished through advanced techniques like Machine Learning (ML) integrated with optimization algorithms. The purpose of this research is to develop an ML model for estimating the performance of a metamaterial antenna (gain and bandwidth). The Particle Swarm Optimization heuristic algorithm is used to optimize the hyperparameters of the ML models (Random Forest and Support Vector Machine). The ML models were evaluated using a comparison table in terms of R2, MAE, MSE, and RMSE. The PSO-SVM hybrid model showed better predictive performance than PSO-RF, with MSEs of 0.0003 for gain and 0.0020 for bandwidth. The optimized ML models can be used to predict antenna gain and bandwidth more accurately.

Özet

Modern haberleşme sistemlerinde sürekli olarak daha iyi performans talebi, elektromanyetik bileşen tasarımında güncel gelişmelere yol açmıştır. Metamalzeme birim hücre yapılarının anten tasarımlarına entegre edilmesi, antenlerin elektromanyetik ışıma özelliklerini iyileştirmek için bir çözüm olabilir. Ayrıca, anten tasarımı sürecinde, hem çoklu performansların (boyut, bant genişliği, kazanç ve geri dönüş kaybı gibi) elde edilmesi hem de zaman verimliliği giderek daha önemli hale gelmiştir ve bu, optimizasyon algoritmaları entegre edilmiş Makine Öğrenimi (ML) gibi gelişmiş teknikler aracılığıyla gerçekleştirilmektedir. Bu çalışma, bir metamalzeme antenin performansını (kazanç ve bant genişliği) tahmin etmek için bir ML modeli geliştirmeyi amaçlamaktadır. ML modellerinin (Rastgele Orman ve Destek Vektör Makinesi) hiperparametrelerini optimize etmek için Parçacık Sürü Optimizasyonu sezgisel algoritması kullanılmıştır. ML modelleri, R2, MAE, MSE ve RMSE açısından bir karşılaştırma tablosu kullanılarak değerlendirilmiştir. PSO-SVM hibrit modeli, PSO-RF ile karşılaştırıldığında daha iyi tahmin performansı (kazanç için MSE = 0.0003, bant genişliği için MSE = 0.0020) göstermiştir. Optimize edilmiş ML modelleri anten kazancını ve bant genişliğini daha doğru bir şekilde tahmin etmek için kullanılabilir.

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Cite This Article

Yalçın, N., Ünaldı, S., Çakır, S. (2026). Enhanced Prediction of Gain and Bandwidth for Metamaterial Antenna Using Machine Learning and Metaheuristic Optimization. Karaelmas Fen ve Mühendislik Dergisi, Cilt: 16 Sayı: 1, 49–56. https://doi.org/10.7212/karaelmasfen.1779178

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