Publication detail

Comparison of a Real-Coding Genetic Algorithm and Particle Swarm Optimization on a Band Gap Bandwidth Maximization Problem

Lukáš Oliva, Viktor Otevřel, Zbyněk Raida

Original Title

Comparison of a Real-Coding Genetic Algorithm and Particle Swarm Optimization on a Band Gap Bandwidth Maximization Problem

English Title

Comparison of a Real-Coding Genetic Algorithm and Particle Swarm Optimization on a Band Gap Bandwidth Maximization Problem

Type

conference paper

Language

en

Original Abstract

In this paper, two global optimization algorithms, particle swarm optimization (PSO) and mean-adaptive real-coding genetic algorithm (MAD-RCGA) are applied to a problem of optimizing non-traditional dielectric electromagnetic band gap structures (EBG). The problem is formulated in nine dimensions with the goal of finding as large frequency gap between the first and the second TM bands as possible. Maximizing the frequency gap enables to improve properties of planar patch antennas in wide band.

English abstract

In this paper, two global optimization algorithms, particle swarm optimization (PSO) and mean-adaptive real-coding genetic algorithm (MAD-RCGA) are applied to a problem of optimizing non-traditional dielectric electromagnetic band gap structures (EBG). The problem is formulated in nine dimensions with the goal of finding as large frequency gap between the first and the second TM bands as possible. Maximizing the frequency gap enables to improve properties of planar patch antennas in wide band.

Keywords

Global optimization, Real-coding genetic algorithm, Particle swarm optimization.

RIV year

2007

Released

14.05.2007

Publisher

Budapest University of Technology and Economics

Location

Budapest

Pages from

128

Pages to

258

Pages count

131

BibTex


@inproceedings{BUT22358,
  author="Lukáš {Oliva} and Viktor {Otevřel} and Zbyněk {Raida}",
  title="Comparison of a Real-Coding Genetic Algorithm and Particle Swarm Optimization on a Band Gap Bandwidth Maximization Problem",
  annote="In this paper, two global optimization algorithms, particle swarm optimization (PSO) and mean-adaptive real-coding genetic algorithm (MAD-RCGA) are applied to a problem of optimizing non-traditional dielectric electromagnetic band gap structures (EBG). The problem is formulated in nine dimensions with the goal of finding as large frequency gap between the first and the second TM bands as possible. Maximizing the frequency gap enables to improve properties of planar patch antennas in wide band.",
  address="Budapest University of Technology and Economics",
  booktitle="proceedings of Microcoll 2007",
  chapter="22358",
  institution="Budapest University of Technology and Economics",
  year="2007",
  month="may",
  pages="128",
  publisher="Budapest University of Technology and Economics",
  type="conference paper"
}