Title: Intelligent genetic algorithms in the optimisation of a PIFA antenna using hybridised fitness characterisation and clustering

Authors: Mohammad Riyad Ameerudden; Harry Coomar Shumsher Rughooputh

Addresses: Department of Electronics and Communication Engineering, University of Mauritius, Reduit, Mauritius ' Department of Electronics and Communication Engineering, University of Mauritius, Reduit, Mauritius

Abstract: With the exponential development of mobile communications and the miniaturisation 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 seeks to investigate the performance an intelligent optimisation technique using a hybridised genetic algorithms (GA) coupled with the intelligence of the binary string fitness characterisation (BSFC) technique. The aim of this project is to design and optimise the bandwidth of a planar inverted-F antenna (PIFA) in order to achieve a larger bandwidth in the 2 GHz band. The optimisation process has been enhanced by using a clustering algorithm to minimise the computational cost. The convergence pattern was compared with the particle swarm optimisation (PSO) technique. During the optimisation process, the different PIFA models are evaluated using the finite-difference time domain (FDTD) method.

Keywords: genetic algorithms; hybrid GAs; clustering algorithms; binary string fitness characterisation; BSFC; planar inverted-F antennas; PIFA; finite difference time domain; FDTD; intelligent optimisation; antenna bandwidth; convergence pattern; particle swarm optimisation; PSO; mobile communications; mobile frequencies.

DOI: 10.1504/IJENM.2012.051311

International Journal of Enterprise Network Management, 2012 Vol.5 No.3, pp.272 - 280

Published online: 16 Aug 2014 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article