Inset-fed microstrip patch antenna optimization for 2.4 GHz using surrogate model assisted differential evolution machine learning algorithm

In this work, we have used the surrogate model assisted differential evolution (SADEA) to model a one and two-element inset-fed patch antenna array to optimize its parameters for efficiency and usability. The microstrip patch antennas operates in a frequency band of 2.4 GHz. The optimization process focused on fine-tuning the patch length, patch width, and notch width to enhance key performance metrics directivity, return loss, and bandwidth. The design is made in CST software with an FR-4 substrate and simulated in the ADE1.0 software a MATLAB toolbox. Significant enhancements were achieved including a directivity gain of 3.04 dB, and 5.58 dB a return loss of -19 dB, -16 dB, and an expanded impedance bandwidth from 0.0798 GHz, 0.0588 GHz to 0.0951 GHz, 0.0824 GHz respectively. The antenna was constructed and then measured. The findings showed that the measurements and the fabrication process closely matched, especially in terms of return loss.