A New Jumping Genes Paradigm for an E-Shaped Folded Patch Feed Antenna Design

Abstract

A novel evolutionary computing algorithm, namely, jumping genes evolutionary algorithm (JGEA) is used for the optimization of antenna designs. This scheme incorporates with a multiobjective strategy that enables the gene mobility within the same chromosome, or even to a different chromosome. This type of horizontal gene movement causes the genes to find the suitable locations to achieve the necessary building blocks in such a way that the quality of nondominated solutions and/or the Pareto-optimal solutions can be enhanced. This new scheme is robust and provides outputs in speed and accuracy. It also generates a range of widespread extreme solutions. The design of an E-shaped patch antenna was adopted for the purpose of design demonstration. An antenna structure with 91% impedance bandwidth for a frequency range of 3.6–9.6 GHz was selected amongst the nondominated solutions set for the hardware fabrication. Its measured performances both for impedance bandwidth and frequency range were in good agreement with the simulated solution. The cross-polarized field was found to be small in comparison, and the copolarized field can sustain the broadside radiation pattern over the frequency band. This methodology of optimization can be of an alternative approach for antenna design.