Advance Artificial Intelligence Technique for Designing Double T-Shaped Monopole Antenna

Machine learning (ML) has taken the world by a tornado with its prevalent applications in automating ordinary tasks and using turbulent insights throughout scientific research and design strolls. ML is a massive area within artificial intelligence (AI) that focuses on obtaining valuable information out of data, explaining why ML has often been related to stats and data science. An advanced meta-heuristic optimization algorithm is proposed in this work for the optimization problem of antenna architecture design. The algorithm is designed, depending on the hybrid between the Sine Cosine Algorithm (SCA) and the Grey Wolf Optimizer (GWO), to train neural network-based Multilayer Perceptron (MLP). The proposed optimization algorithm is a practical, versatile, and trustworthy platform to recognize the design parameters in an optimal way for an endorsement double T-shaped monopole antenna. The proposed algorithm likewise shows a comparative and statistical analysis by different curves in addition to the ANOVA and T-Test. It offers the superiority and validation stability evaluation of the predicted results to verify the procedures’ accuracy.