Hybridized Genetic Algorithms in the Optimization of a PIFA Antenna Using Fitness Characterization and Clustering

With the exponential development of mobile communications and the miniaturization of radio frequency transceivers, the need for small and low profile antennas at mobile frequencies is constantly growing. Therefore, new antennas should be developed to provide both larger bandwidth and small dimensions. The aim of this project is to design and optimize the bandwidth of a Planar Inverted-F Antenna (PIFA) in order to achieve a larger bandwidth in the 2 GHz band. This paper presents an intelligent optimization technique using a hybridized Genetic Algorithms (GA) coupled with the intelligence of the Binary String Fitness Characterization (BSFC) technique. The optimization technique used is based on the Binary Coded GA (BCGA) and Real-Coded GA (RCGA). The process has been further enhanced by using a Clustering Algorithm to minimize the computational cost. Using the Hybridized GA with BSFC and Clustering, the bandwidth evaluation process has been observed to be more efficient combining both high performance and minimal computational cost. During the optimization process, the different PIFA models are evaluated using the finite-difference time domain (FDTD) method.