Abstract

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.