Abstract—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 an intelligent optimization technique using a hybridized Genetic Algorithms (GA) coupled with the intelligence of the Binary String Fitness Characterization (BSFC) technique. 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. The optimization technique used is based on the Binary Coded GA (BCGA) and Real-Coded GA (RCGA). The optimization process has been enhanced by using a Clustering Algorithm to minimize the computational cost. During the optimization process, the different PIFA models are evaluated using the finite-difference time domain (FDTD) method.
Index Terms—BSFC, clustering, genetic algorithms, hybrid, intelligent computing.
Mohammad Riyad Ameerudden is with the University of Mauritius, Mauritius (e-mail: riyadxxx@ intent.mu).
Harry C. S. Rughooputh is with the Department of Electronics and Communications, University of Mauritius, Mauritius (e-mail: email@example.com).
Cite:Mohammad Riyad Ameerudden and Harry C. S. Rughooputh, "Hybrid BSCF Genetic Algorithms in the Optimization of a PIFA Antenna," International Journal of Machine Learning and Computing vol.2, no. 6, pp. 746-749, 2012.