Smart hybrid Genetic Algorithms in the bandwidth optimization of a PIFA antenna

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. This paper presents a smart optimization technique using a hybridized Genetic Algorithms (GA) and comparison with more classical GA techniques. The hybridization involves primarily a clustering mechanism coupled with the intelligence of the Binary String Fitness Characterization (BSFC) technique. The optimization engine is applied to the design of a Planar Inverted-F Antenna (PIFA) in order to achieve an optimal bandwidth performance in the 2 GHz band. During the optimization process, the PIFA is modeled and evaluated using the finite-difference time domain (FDTD) method.