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

被引：57

作者：

Zhang, Zhen ^{[1
,2
]}

Chen, Hong Cai ^{[1
]}

Cheng, Qingsha S. ^{[1
,3
]}

机构：

[1] Southern Univ Sci & Technol, Dept Elect & Elect Engn, Shenzhen 518055, Peoples R China

[2] Harbin Inst Technol, Harbin 150001, Peoples R China

[3] Southern Univ Sci & Technol, Key Lab Adv Wireless Commun Guangdong Prov, Shenzhen 518055, Peoples R China

来源：

IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION | 2021年 / 69卷 / 05期

基金：

中国国家自然科学基金;

关键词：

Antennas; Sampling methods; Computational modeling; Convergence; Heuristic algorithms; Optimization methods; Antenna design; heuristic hypersphere sampling (HHS); surrogate-assisted optimization; DIFFERENTIAL EVOLUTION; MONOPOLE ANTENNA; DESIGN; ALGORITHM;

D O I：

10.1109/TAP.2020.3031474

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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.

引用

页码：2993 / 2998

页数：6

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