Intelligent genetic algorithms in the optimisation of a PIFA antenna using hybridised fitness characterisation and clustering
by Mohammad Riyad Ameerudden; Harry Coomar Shumsher Rughooputh
International Journal of Enterprise Network Management (IJENM), Vol. 5, No. 3, 2012

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.

Online publication date: Sat, 16-Aug-2014

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