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:
r.rughooputh@uom.ac.mu).
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.