Abstract
Defected ground structure (DGS) is a technique for increasing antenna gain by changing the shape of the ground, without the need to increase the dimensions of the antenna. However, the application of the DGS technique is generally carried out using an inductive approach which requires high computational resources and time consuming for the design process. Therefore, to speed up the DGS design process, machine learning methods, especially genetic algorithms, is used. This study proposes a patch antenna DGS optimization model to increase the patch antenna gain using genetic algorithm so that the DGS design time can be shortened and the design process efficiency can be increased. Based on the simulation results, the DGS design without genetic algorithm is able to increase the bandwidth and gain of the patch antenna by 8.91% and 3.92%, respectively. Meanwhile, the DGS design optimized by genetic algorithm is able to increase the bandwidth and gain of the patch antenna by 84% and 50.86%, respectively. In addition, shorter optimization time is achieved by using genetic algorithm.
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