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Optimized Ensemble Algorithm for Predicting Metamaterial Antenna Parameters

El-Sayed M. El-kenawy1,2, Abdelhameed Ibrahim3,*, Seyedali Mirjalili4,5, Yu-Dong Zhang6, Shaima Elnazer7,8, Rokaia M. Zaki9,10

1 Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura, 35111, Egypt
2 Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura, 35712, Egypt
3 Computer Engineering and Control Systems Department, Faculty of Engineering, Mansoura University, Mansoura, 35516, Egypt
4 Centre for Artificial Intelligence Research and Optimization, Torrens University Australia, Fortitude Valley, QLD 4006, Australia
5 Yonsei Frontier Lab, Yonsei University, Seoul, 03722, Korea
6 School of Computing and Mathematical Sciences, University of Leicester, Leicester, LE1 7RH, UK
7 Nile Higher Institute for Engineering and Technology, Mansoura, Egypt
8 Computer and Information Technology College, Taif University, Taif, Saudi Arabia
9 Higher Institute of Engineering and Technology, Kafrelsheikh
10 Department of Electrical Engineering, Shoubra Faculty of Engineering, Benha University, Egypt

* Corresponding Author: Abdelhameed Ibrahim. Email: email

(This article belongs to this Special Issue: Artificial Intelligence and Machine Learning Algorithms in Real-World Applications and Theories)

Computers, Materials & Continua 2022, 71(3), 4989-5003.


Metamaterial Antenna is a subclass of antennas that makes use of metamaterial to improve performance. Metamaterial antennas can overcome the bandwidth constraint associated with tiny antennas. Machine learning is receiving a lot of interest in optimizing solutions in a variety of areas. Machine learning methods are already a significant component of ongoing research and are anticipated to play a critical role in today's technology. The accuracy of the forecast is mostly determined by the model used. The purpose of this article is to provide an optimal ensemble model for predicting the bandwidth and gain of the Metamaterial Antenna. Support Vector Machines (SVM), Random Forest, K-Neighbors Regressor, and Decision Tree Regressor were utilized as the basic models. The Adaptive Dynamic Polar Rose Guided Whale Optimization method, named AD-PRS-Guided WOA, was used to pick the optimal features from the datasets. The suggested model is compared to models based on five variables and to the average ensemble model. The findings indicate that the presented model using Random Forest results in a Root Mean Squared Error (RMSE) of (0.0102) for bandwidth and RMSE of (0.0891) for gain. This is superior to other models and can accurately predict antenna bandwidth and gain.


Cite This Article

E. M. El-kenawy, A. Ibrahim, S. Mirjalili, Y. Zhang, S. Elnazer et al., "Optimized ensemble algorithm for predicting metamaterial antenna parameters," Computers, Materials & Continua, vol. 71, no.3, pp. 4989–5003, 2022.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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