Design of Metamaterial Inspired Antenna Using Machine Learning and Optimization Techniques

Now-a-days, the antenna design process is facing challenges and it involves compromise between antenna structure including dimensions and overall performance parameters such as Gain, Bandwidth, Return Loss, and Voltage Standing Wave Ratio (VSWR). Most of the antennas are designed by using optimization methods which focus only on antenna size reduction, but the overall performance optimization of antenna is not much focused. Because satisfying many different contradictory performance requirements are difficult in antenna optimization. Hence, this work analyzes the formulation of a better objective function for satisfying different performance requirements of a metamaterial antenna. In this work, an antenna model is developed using Artificial Neural Network (ANN) and optimization of the same is done by using suitable objective function for the antenna model. To analyze the right choice of objective function four different single objective functions with constraints are formulated and its effectiveness has been explored. Further, the popular Evolutionary Algorithm Particle Swarm optimization (PSO) method is employed to optimize the ANN based metamaterial antenna parameters. The minimizing objective function with constraints shows better results compared to maximizing objective function. Also, it is inferred that more than one performance requirements can be specified as constraints. This kind of optimization helps to satisfy the overall performance of the antenna.