Open Access

ARTICLE

An Optimized Ensemble Model for Prediction the Bandwidth of Metamaterial Antenna

by Abdelhameed Ibrahim^1,*, Hattan F. Abutarboush², Ali Wagdy Mohamed^3,4, Mohamad Fouad¹, El-Sayed M. El-kenawy^5,6

1 Computer Engineering and Control Systems Department, Faculty of Engineering, Mansoura University, Mansoura, 35516, Egypt
2 Electrical Engineering Department, College of Engineering, Taibah University, Medina, 42353, Saudi Arabia
3 Operations Research Department, Faculty of Graduate Studies for Statistical Research, Cairo University, Giza, 12613, Egypt
4 Wireless Intelligent Networks Center (WINC), School of Engineering and Applied Sciences, Nile University, Giza, Egypt
5 Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura, 35111, Egypt
6 Faculty of Artificial Intelligence, Delta University for Science and Technology, Egypt

* Corresponding Author: Abdelhameed Ibrahim. Email:

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

Computers, Materials & Continua 2022, 71(1), 199-213. https://doi.org/10.32604/cmc.2022.021886

Received 18 July 2021; Accepted 19 August 2021; Issue published 03 November 2021

Abstract

Metamaterial Antenna is a special class of antennas that uses metamaterial to enhance their performance. Antenna size affects the quality factor and the radiation loss of the antenna. Metamaterial antennas can overcome the limitation of bandwidth for small antennas. Machine learning (ML) model is recently applied to predict antenna parameters. ML can be used as an alternative approach to the trial-and-error process of finding proper parameters of the simulated antenna. The accuracy of the prediction depends mainly on the selected model. Ensemble models combine two or more base models to produce a better-enhanced model. In this paper, a weighted average ensemble model is proposed to predict the bandwidth of the Metamaterial Antenna. Two base models are used namely: Multilayer Perceptron (MLP) and Support Vector Machines (SVM). To calculate the weights for each model, an optimization algorithm is used to find the optimal weights of the ensemble. Dynamic Group-Based Cooperative Optimizer (DGCO) is employed to search for optimal weight for the base models. The proposed model is compared with three based models and the average ensemble model. The results show that the proposed model is better than other models and can predict antenna bandwidth efficiently.

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