High Directivity Microstrip Patch Antenna design using Binary Ebola Search Optimization for Radio Frequency Identification Application

Abstract

Microstrip patch antenna has gained significant popularity in wireless communication field, because of its compact size, less profile, and ease of installation. It is widely used in various applications such as Radio-Frequency Identification (RFID) systems, where dependable and long-range communication is made possible by the great directivity and efficiency. In this manuscript, a High Directivity Microstrip Patch Antenna design using Binary Ebola Search Optimization for Radio Frequency Identification Application (MPA-RFID-BESO) is proposed.The design of microstrip patch antennas is the difficulty in achieving high directivity while maintaining compact size and low profile. This research explains design and application-specific optimisation of the Microstrip Patch Antenna (MPA) of biomedical utilizing Binary Ebola Search optimization (BESO) algorithm. Microstrip patch antenna is designed to function in double and multi band applications due to its low cost, light weight, and ease of installation. MPA is made with flawed ground construction to lessen cross-polarized radiation emitted by microstrip patches and to achieve the necessary radiation parameters. Here, the cost of the materials is reduced by using a liquid crystal polymer substrate, and aerial performance is enhanced by using the appropriate geometrical parameters. The small design of the BESO optimised improves the performance of antenna as 50mm×50mm compact size. The MATLAB program uses high frequency to do the simulation method. The proposed antenna design is a preferable option for applications involving Radio Frequency Identification (RFID). The performance metrics, such as radiation pattern, constant gain, directivity, bandwidth, and antenna efficiency and return loss are measured and compared to the existing techniques. The proposed MPA-BA-BESO design provides 10.51%, 12.85% and 16.04% higher bandwidth and 23.63%, 47.86% and 32.06% higher gain compared with existing designs, like Designing MPA utilizing Moth–Flame optimization approach (MPA-UWB-MFOA), Predicting Rectangular Patch Microstrip Antenna Dimension utilizing Machine Learning (MPA-ANN), Dual Band Antenna Design and Prediction of Resonance Frequency Utilizing Machine Learning Algorithms (DBAD-RF-CNN) respectively. The proposed technology serve as a better design alternative for microstrip patch antennas in communication systems to address biological applications.