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IJMLC 2012 Vol.2(6): 746-749 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2012.V2.228

Hybrid BSCF Genetic Algorithms in the Optimization of a PIFA Antenna

Mohammad Riyad Ameerudden and Harry C. S. Rughooputh

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.

General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • Frequency: Quaterly
  • DOI: 10.18178/IJML
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijml@ejournal.net

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