Antenna design has grown more stringent and difficult over the years as the world becomes strictly a wireless environment. The inherent tradeoffs that exist between gain, radiation pattern, bandwidth, and physical size and the multiple parameters that must be considered make antenna design a lengthy and tedious process. Methods have been devised which automate this complex process of antenna optimization through the use of genetic algorithms, particle swarm optimization, and simulated annealing. Genetic algorithms are capable of handling a large number of design parameters and work for optimization problems that have discontinuous or non-differentiable multi-dimensional solution spaces, making them ideal for antenna optimization. In the present work, a genetic algorithm has been used for size reduction in microstrip patch antennas and design tradeoff optimization between beamwidth and gain in helical antennas. A method for reducing the size of microstrip patch antennas by up to 75% by removing rectangular and circular slots from the metal of the microstrip patch is presented. A solid patch antenna that resonates at 10 GHz is forced to resonant at 6 GHz through the removal of the different shaped slots. Given the number and shape of the slots, the genetic algorithm is used to optimize the size and location of the slot on the patch. The designs are obtained by interfacing the genetic algorithm and Ansoft High Frequency System Simulator (HFSS) and validated through design, construction, and testing. High gain, with broad half-power beamwidths (HPBW) is traditionally extremely difficult to achieve due to the inherent tradeoff between the two. A genetic algorithm has been applied to design a helical antenna with a gain of 10 dB and HPBWs of 60 degrees. In order to achieve this, three physical parameters of the helix have been changed, namely the pitch, helix radius, and the ground plane geometry. The second objective is to create an antenna that displays different HPBWs in the two radiation planes. This could be extremely useful in many communication environments and there is yet no existing method to achieve this. The genetic algorithm produced a helical antenna that shows a 19 degrees difference in HPBW between the two radiation planes, while still displaying a 7 dB gain and low side lobes. Numerical Electromagnetic Code 4 (NEC4) is used, and a method of communication between MATLAB and NEC4 has been developed to make the genetic algorithm optimization possible.
Library of Congress Subject Headings
Antennas (Electronics)--Design and construction--Data processing; Genetic algorithms
Department, Program, or Center
Electrical Engineering (KGCOE)
Venkataraman, Jayanti - Chair
Wyant, Andrea, "Genetic algorithm optimization applied to planar and wire antennas" (2007). Thesis. Rochester Institute of Technology. Accessed from
RIT – Main Campus