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

Mohammad Riyad Ameerudden and Harry Coomar Shumsher Rughooputh

International Journal of Enterprise Network Management, 2012, vol. 5, issue 3, 272-280

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. (search for similar items in EconPapers)
Date: 2012
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=51311 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijenma:v:5:y:2012:i:3:p:272-280

Access Statistics for this article

More articles in International Journal of Enterprise Network Management from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().