Application of Genetic Algorithms in the Design of Hybrid Fractal Microstrip Antennas Based on Minkowski and Sierpinski Carpet Patterns

Abstract

With wireless multifrequency applications, this research paper presents how to use genetic algorithms (GA) to design a hybrid fractal microstrip antenna based on the Minkowski and Sierpinski carpet fractal microstrip structures. The traditional design of fractal antennas uses the iterated function system (IFS), which has difficulty in finding flexible dimensional scales to match every desired operating frequency. GA can generate dimensional scales flexibly with constraints to control desired antenna parameters. This allows for maintaining control over dimensional scales when designing complex antenna shapes. Different shapes of patch antenna are optimized using GA with dynamic changes to the antenna geometry at each desired operating frequency. The optimization and simulation were conducted using the matrix laboratory (MATLAB) programming language and computer simulation technology (CST) microwave studio programs. The desired frequencies operate in the frequency range of 0.7 - 2.6 GHz. The antenna configurations were optimized to achieve resonance and transformation frequencies, and the microstrip feedlines were matched to minimize return losses of less than -15 dB. Both simulated and experimental results for the antenna design are presented. Finally, the crucial requirements of the antenna design and the proposed GA optimization are summarized.