Surrogate-Assisted Quasi-Newton Enhanced Global Optimization of Antennas Based on a Heuristic Hypersphere Sampling

This communication presents a novel surrogate-assisted quasi-Newton enhanced global optimization (SA-QNEGO) algorithm. In this proposed method, the heuristic hypersphere sampling (HHS) method is used to obtain representative samples. The surrogate model is built based on the low-fidelity model. The quasi-Newton enhanced differential evolution (DE) method is designed to optimize the surrogate model. Finally, the optimal design of a high-fidelity model is obtained through a space mapping procedure. The proposed algorithm is verified through two antenna design examples including a dipole antenna with balun and an SIW cavity-backed slot antenna. The results show that the proposed algorithm finds a more accurate minimum value with less computational time than direct optimization using DE.