A Simulation-Driven Surrogate Parallel Improved AGA Method for the Automated Design of Antenna

Abstract

A simulation-driven surrogate parallel improved adaptive genetic algorithm (SDS-IAGA) method is proposed. This method aims to improve the efficiency of topology optimization for the automated design of antenna. The optimization process involves two stages: initialization, population screening and algorithm application. In the first stage, a coarse-mesh electromagnetic (EM) simulation model combined with a current-driven search is utilized to provide a high-quality initial population. In the second stage, variable-fidelity surrogate and correction technology assist the IAGA in optimizing the antenna topology. During this stage, the IAGA uses new adaptive crossover and mutation operators based on nonlinear improvement to enhance the efficiency in reaching the target solution. To verify the efficacy of the proposed SDS-IAGA, the design task of a planar tri-band antenna with center frequencies at 2.45 GHz/3.5 GHz/5.8 GHz is completed. The experimental results demonstrate that, compared to AGA and IAGA, the SDS-IAGA enhances the optimization efficiency of antenna topology by 62.97% and 54.22%, respectively. Furthermore, compared to existing optimization methods, SDS-IAGA can complete the target design task with fewer full-wave EM simulations.