Improved Prediction of Metamaterial Antenna Bandwidth Using Adaptive Optimization of LSTM

The design of an antenna requires a careful selection its parameters to retain the desired performance. However, this task is time-consuming when traditional approaches are employed, which represents significant challenge. On other hand, machine learning presents effective solution challenge through set regression models that can robustly assist designers find out best achieve intended In paper, we propose novel approach for accurately predicting bandwidth metamaterial antenna. proposed based on employing recently emerged guided whale optimization algorithm using adaptive particle swarm optimize long-short-term memory (LSTM) deep network. This optimized network used retrieve given features. addition, superiority examined in terms comparison with multilayer perceptron (ML), K-nearest neighbors (K-NN), and basic LSTM several evaluation criteria such as root mean square error (RMSE), absolute (MAE), bias (MBE). Experimental results show could RMSE (0.003018), MAE (0.001871), MBE (0.000205). These values better than those competing models.