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