Surrogate Based Design Optimization of Multi-Band Antenna

In this work, design optimization process of a multiband antenna via the use of Artificial Neural Network (ANN) based surrogate model and meta-heuristic optimizers is studied. For this mean firstly, by using Latin-Hyper cube sampling method a data set based on 3D full wave EM simulator is generated to train an ANN based model. By using the ANN based surrogate model and a meta-heuristic optimizer Invasive Weed Optimization (IWO), design optimization of a multi-band antenna for (I) 2.4-3.6 GHz for ISM, LTE, and 5G sub frequencies, (II) 9–10 GHz for X band applications is aimed. Then the obtained results are compared with the simulated results of 3D EM simulation tool CST. Results show, that the proposed methodology provides a computationally efficient design optimization process for design optimization of multi-band antennas.