Optimization Design of Metamaterial Absorbers Based on an Improved Adaptive Genetic Algorithm

Authors

  • Sai Sui College of Science Air Force Engineering University, Xi'an, Shaanxi 710051, China
  • Hua Ma College of Science Air Force Engineering University, Xi'an, Shaanxi 710051, China
  • Hong-Wei Chang College of Science Air Force Engineering University, Xi'an, Shaanxi 710051, China
  • Jia-Fu Wang College of Science Air Force Engineering University, Xi'an, Shaanxi 710051, China
  • Zhuo Xu Electronic Materials Research Laboratory, Key Laboratory of the Ministry of Education Xi'an Jiao tong University Xi'an, Shaanxi 710049, China
  • Shao-Bo Qu College of Science Air Force Engineering University, Xi'an, Shaanxi 710051, China

Keywords:

Absorber, adaptive genetic algorithm, metamaterial, optimization

Abstract

Most reported metamaterials are designed empirically by parameter sweep, which is time-consuming and ineffective. We propose an optimization method of designing metamaterial absorbers based on an improved adaptive genetic algorithm (IAGA), with the aim to get wideband absorption. Firstly, an IAGA optimization model is presented, of which the crossover probability is adaptively adjusted by introducing a nonlinear function, and the mutation probability is adaptively adjusted using complementary idea. Then, a wideband triple-layer metamaterial absorber in THz region is designed and optimized using IAGA, getting about 40.4% increasing of relative bandwidth compared with the results of reference [19]. A further comparison between IAGA and standard genetic algorithm (SGA) indicates that the IAGA is an effective method in improving convergence speed and stability, and can be used to optimize structure parameters of metamaterial absorbers with desired characteristics.

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Published

2019-08-01

How to Cite

Sai Sui, Hua Ma, Hong-Wei Chang, Jia-Fu Wang, Zhuo Xu, & Shao-Bo Qu. (2019). Optimization Design of Metamaterial Absorbers Based on an Improved Adaptive Genetic Algorithm. The Applied Computational Electromagnetics Society Journal (ACES), 34(08), 1198–1203. Retrieved from https://journals.riverpublishers.com/index.php/ACES/article/view/8291

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