ENHANCING MICROSTRIP PATCH ANTENNA PERFORMANCE THROUGH GENETIC ALGORITHM AND ANFIS OPTIMIZATION

Microstrip patch antennas play an important role in modern wireless communication systems due to their small size, compactness, lightweight, and easy integration into electronic circuits. These types of antennas are widely used in various applications, such as mobile phones, Wi-Fi routers, satellite communications internal systems and radar systems. This paper provides a detailed research study on the improvement and optimization of the measurement of the Aperture Coupled Microstrip Patch Antenna (ACMPA) utilizing a Genetic Algorithm (GA) as well as an Adaptive Neuro-Fuzzy Inference System (ANFIS). The goal is to enhance the total efficiency of the antenna by achieving far better return loss and high directivity coupled with a reduced Voltage Standing Wave Ratio (VSWR). The recommended method includes the usage of GA within the CST software application together with ANFIS within the MATLAB software application to enhance the ACMPA measurements for the procedure at 7.5 GHz. The outcomes show the performance of the recommended antenna measurements in contrast with substitute information in the CST program, verifying the precision as well as the effectiveness of the optimization procedure. This research study offers beneficial insights into the application of innovative optimization methods for the style coupled with the improvement of microstrip patch antennas in contemporary cordless interaction systems.