Performance comparison of bio-inspired optimization algorithms for Sierpinski gasket fractal antenna design

Abstract

The Sierpinski gasket fractal antenna is most popular structure in the domain of fractal antennas. This fractal antenna has multi-band performance, and hence, the design of this antenna for the desired frequencies is a challenging problem. The artificial intelligence tools like artificial neural networks, fuzzy logic systems, bio-inspired optimization techniques are appropriate to provide accurate design solution in such cases. In this paper, three most popular bio-inspired optimization algorithms: genetic algorithms, particle swarm optimization (PSO), and bacterial foraging optimization, have been proposed to solve the design issues of Sierpinski gasket pre-fractal antenna. Their performances are analyzed and are compared with the experimental results. A simplified expression for calculation of resonant frequency of Sierpinski gasket pre-fractal antenna is proposed and is used as the objective function. Finally, the effectiveness is compared on the basis of three different measures: mean absolute percentage error, the average time taken by the models to evaluate the results, and the coefficient of correlation. The results indicate that the PSO algorithm is most suitable for this type of antenna.