Optimized Two-Level Ensemble Model for Predicting the Parameters of Metamaterial Antenna

Abstract

Employing machine learning techniques in predicting the parameters of metamaterial antennas has a significant impact on the reduction of the time needed to design an antenna with optimal parameters using simulation tools. In this paper, we propose a new approach for predicting the bandwidth of metamaterial antenna using a novel ensemble model. The proposed ensemble model is composed of two levels of regression models. The first level consists of three strong models namely, random forest, support vector regression, and light gradient boosting machine. Whereas the second level is based on the ElasticNet regression model, which receives the prediction results from the models in the first level for refinement and producing the final optimal result. To achieve the best performance of these regression models, the advanced squirrel search optimization algorithm (ASSOA) is utilized to search for the optimal set of hyper-parameters of each model. Experimental results show that the proposed two-level ensemble model could achieve a robust prediction of the bandwidth of metamaterial antenna when compared with the recently published ensemble models based on the same publicly available benchmark dataset. The findings indicate that the proposed approach results in root mean square error (RMSE) of (0.013), mean absolute error (MAE) of (0.004), and mean bias error (MBE) of (0.0017). These results are superior to the other competing ensemble models and can predict the antenna bandwidth more accurately.