Bayesian Optimization Based on Student’s T Process for Microstrip Antenna Design

Authors

  • Qing Li Ocean College, Jiangsu University of Science and Technology, Zhenjiang 212003, China
  • Fei Meng School of Information and Communication Engineering, Guangzhou Maritime University, Guangzhou 510725, China
  • Yubo Tian School of Information and Communication Engineering, Guangzhou Maritime University, Guangzhou 510725, China
  • Xiaoyan Wang Ocean College, Jiangsu University of Science and Technology, Zhenjiang 212003, China

DOI:

https://doi.org/10.13052/2022.ACES.J.370804

Keywords:

acquisition function, Antenna optimization, Bayesian Optimization, Gaussian process, Student’s T process

Abstract

Bayesian Optimization (BO) is an efficient global optimization algorithm, which is widely used in the field of engineering design. The probabilistic surrogate model and acquisition function are the two keys to the algorithm. Building an efficient probabilistic surrogate model and designing a collection function with excellent exploring capabilities can improve the performance of BO algorithm, allowing it to find the optimal value of the objective function with fewer iterations. Due to the characteristics of small samples and non-parametric derivation of the Gaussian Process (GP), traditional BO algorithms usually use the GP as a surrogate model. Compared with the GP, the Student’s T Process (STP) retains the excellent properties of GP, and has more flexible posterior variance and stronger robustness. In this paper, STP is used as the surrogate model in BO algorithm, the hyperparameters of the model are optimized by STP, and the estimation strategy function (EST) is improved based on the posterior output of the optimized STP, thus realizing the improved BO algorithm based on the STP. To verify the performance of the proposed algorithm, numerical experiments are designed to compare the performances of the traditional BO algorithm, which includes the lower confidence bound function (LCB) and EST as acquisition function respectively and GP as the surrogate model, and the proposed BO algorithm with STP as the surrogate model and LCB, expected improvement function (EI), expected regret minimization function (ERM) as acquisition function respectively. The results show that the proposed algorithm in this paper performs well when finding the global minimum of multimodal functions. Based on the developed algorithm in this paper, the resonant frequency of printed dipole antenna and E-shaped antenna is modeled and optimized, which further confirms the good design ability and design accuracy of the BO algorithm proposed in this paper.

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Author Biographies

Qing Li, Ocean College, Jiangsu University of Science and Technology, Zhenjiang 212003, China

Qing Li was Born in Anqing, Anhui Province, China, studying in Jiangsu University of science and technology, master’s degree, research direction: intelligent optimization algorithm, intelligent electromagnetic optimization.

Fei Meng, School of Information and Communication Engineering, Guangzhou Maritime University, Guangzhou 510725, China

Fei Meng was born in Shenyang, Liaoning Province, China, in 1977. She is currently with the School of Information and Communication Engineering, Guangzhou Maritime University, Guangzhou, China. She has authored and coauthored 10 Journal papers. Her research interest is surrogates and their applications.

Yubo Tian, School of Information and Communication Engineering, Guangzhou Maritime University, Guangzhou 510725, China

Yubo Tian was born in Tieling, Liaoning Province, China, in 1971. He received the Ph.D. degree in radio physics from the De-partment of Electronic Science and Engineering, Nanjing University, Nanjing, China. He has been a visiting scholar at the University of California Los Angeles in 2009 and the Griffith University in 2015, respectively. From 1997 to 2004, he was with the Department of Information Engineering, Shenyang University, Shenyang, China. From 2005 to 2020, he was with the School of Electronics and Information, Jiangsu University of Science and Technology, Zhenjiang, China, where he was a full professor and vice Dean from 2011. He is currently with the School of Information and Communication Engineering, Guangzhou Maritime University, Guangzhou, China. Dr. Tian has authored and coauthored more than 100 Journal papers and 3 books. He also holds more than 20 filed/granted China patents. His current research interest is Machine Learning methods and their applications in electronics and electromagnetics.

Xiaoyan Wang, Ocean College, Jiangsu University of Science and Technology, Zhenjiang 212003, China

Xiaoyan Wang studying in Jiangsu University of science and technology, master’s degree, research direction: intelligent optimization algorithm, intelligent electromagnetic optimization.

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Published

2022-08-31

How to Cite

[1]
Q. . Li, F. . Meng, Y. . Tian, and X. . Wang, “Bayesian Optimization Based on Student’s T Process for Microstrip Antenna Design”, ACES Journal, vol. 37, no. 08, pp. 856–866, Aug. 2022.

Issue

2022: Vol 37 Iss 8

Section

General Submission

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