Boundary restricted genetic algorithm for multi-layer equivalent dielectric parameters retrieval of artificial gradient structure

Shaojun Guo (), Tongsheng Shen () and Chunrong Zou
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Shaojun Guo: National Institute of Defense Technology Innovation, Academy of Military Sciences PLA, China, Fengtai District, Beijing, P. R. China
Tongsheng Shen: National Institute of Defense Technology Innovation, Academy of Military Sciences PLA, China, Fengtai District, Beijing, P. R. China
Chunrong Zou: National Institute of Defense Technology Innovation, Academy of Military Sciences PLA, China, Fengtai District, Beijing, P. R. China

International Journal of Modern Physics C (IJMPC), 2021, vol. 32, issue 01, 1-19

Abstract: With the equivalent dielectric parameters of an artificial gradient structure consisting of a kind of dielectric material as inputs of FDTD multi-layer equivalent simulation, there are big nonuniform differences between the S-curve of retrieval methods and the corresponding full structure. In order to decease these differences, here, a boundary restricted genetic algorithm is proposed. In our method, Smith S method is employed to find the rough values of dielectric parameters, and at the same time, the up and low limit cases are introduced to calculate the boundary parameter values for each layer of the artificial structure and form the searching areas for genetic algorithm to get high-precision inversion of S-curve. The FDTD S-curve of the retrieval parameters and full structure of cone gradient and moth eye were performed experimentally, the maximum deviation of inversion S21 curves corresponding to the cone and moth eye with full structure is limited within 0.0028 and 0.0024 in the X-band (8–12GHz) range, respectively, which shows us the promising application of our method in dielectric parameter retrieval and may be helpful for electromagnetic field analysis.

Keywords: Equivalent dielectric parameter retrieval; S-curve inversion; boundary restricted; genetic algorithm optimization (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1142/S0129183121500042

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