Fractal Antenna Design using Bio-inspired Computing Algorithms

Microstrip patch antennas mainly draw attention to low-power transmitting and receiving applications. These antennas consist of a metal patch (rectangular, square, or some other shape) on a thin layer of dielectric/ferrite (called a substrate) on a ground plane. Microstrip antennas have matured considerably during the past three decades, and many of their limitations have been overcome. As the size of communication devices is decreasing day by day, the demand for miniaturized patch antennas is growing. Many methods of reducing the size of antennas have been developed in the past two decades. The recent trend in this direction is to use fractal geometry. The design of an antenna for a specific resonant frequency requires the calculation of the optimal value of various dimensions. This is a hard task for fractal antennas because the accurate mathematical formulas leading to exact solutions do not exist for the analysis and design of these antennas. The use of bio-inspired computing techniques is gaining momentum in antenna design and analysis due to rapid growth in the computational processing power, and the main techniques are Artificial Neural Network (ANN), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Bacterial Foraging Optimization (BFO), and Swine Influenza Model-based Optimization (SIMBO), etc. In the area of antenna design, the ANNs are employed to model the relationship between the physical and electromagnetic parameters. The trained ANNs are effectively used for the analysis and design of various types of antennas. Bio-inspired optimization techniques have been used by researchers to calculate the optimal parameters of various patch antennas and for the size optimization of antennas. Also, the hybrids of ANN and optimization techniques are proposed as effective algorithms for many applications, especially when the expressions for relating the input and output variables are not available. The presented research has addressed these recent topics by designing miniaturized fractal antennas using bio-inspired computing techniques for various low-power applications, thus, providing cost-effective and efficient solutions.

This chapter is dedicated to bio-inspired computing techniques and their applications in antennas. The working principles of ANN, ANN Ensemble, GA, PSO, and BFO are described, and some hybrid bio-inspired computing techniques are also discussed. The literature survey related to the applications of bio-inspired computing optimization techniques in antennas is given in this chapter. The limitations of the existing bio-inspired computing techniques are highlighted. The existing applications of bio-inspired computing techniques in fractal antennas are also reviewed in this chapter.

This chapter discusses fractal geometry concepts and fractal antennas. Selected fractal antennas and their features are described, and all the designed fractal antennas are introduced in this chapter. The important features like miniaturization & multiband operation of the designed fractal antennas are highlighted, and their applications are also discussed. 

In this chapter, the development of ANN models for the design of proposed fractal antennas is explained. The various parameters of the fractal antennas selected for ANN models are described. The ANN models are designed using feed-forward neural networks, namely MLPNN, RBFNN and GRNN. The performance comparison of different ANN models on the basis of different performance measures is also given. The design of ANN ensemble models for fractal antennas is introduced, and different techniques for developing ANN ensemble models are also discussed in this chapter. 

One of the novel contributions of this book is the development of hybrid bio-inspired computing algorithms for the design of fractal antennas. This work is presented in this chapter. The hybrid algorithms are developed to design the proposed fractal antennas for desired frequencies. The performance comparison of bio-inspired computing algorithms for the design of a multiband Sierpinski Gasket fractal antenna is also explained. The development of various hybrid algorithms like the GA-ANN hybrid Algorithm, BFO-ANN ensemble hybrid Algorithm, and PSO-ANN Ensemble hybrid Algorithm is explained. The use of ANN models as objective functions of optimization algorithms is discussed in this chapter. This chapter also deals with the experimental testing and validation of the developed fractal antennas. The photographs of the fabricated antennas and the experimental results are included. The comparison of the simulated results and experimental results is discussed. The suitability of the designed antennas for different applications is also highlighted in this chapter. 

The conclusion drawn from the research work presented in the book, with some recommendations for future work, is presented in this chapter.