Solving Optimization Problems of Metamaterial and Double T-Shape Antennas Using Advanced Meta-Heuristics Algorithms

This study offers an adaptive dynamic sine cosine fitness grey wolf optimizer (ADSCFGWO) for optimizing the parameters of two types of antennas. The two types of antennas are metamaterial and double T-shape monopoles. The ADSCFGWO algorithm is based on an adaptive dynamic technique and two recently developed and powerful optimization techniques: a modified grey wolf optimization (GWO) based on fitness value and a sine cosine algorithm (SCA). The suggested approach utilizes both algorithms' capabilities to better balance the exploration and exploitation responsibilities of the optimization process while achieving rapid convergence. First, a new feature selection approach is proposed to choose the most significant features from the metamaterial dataset using the suggested ADSCFGWO-based ensemble model for optimal performance. The ADSCFGWO algorithm also optimizes a bidirectional recurrent neural network (BRNN) to estimate the double T-shape monopole antenna characteristics. Several experiments were undertaken to demonstrate the superiority of the suggested algorithms by comparing their results to those of existing optimization algorithms, feature selectors, and regression models. In addition, a statistical analysis is offered to evaluate the algorithm's effectiveness and stability. The findings demonstrate the suggested method's efficacy and superiority over numerous competing algorithms.