Affiliations: [a] College of Electrical Engineering, Zhejiang University, Hangzhou, China | [b] Zhejiang Academy of Special Equipment Science, Hangzhou, China
Correspondence:
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Corresponding author: Yiying Li, College of Electrical Engineering, Zhejiang University, 38 Zheda Road, 310027, Hangzhou, China. E-mail: liyiying@zju.edu.cn
Abstract: A metamaterial (MM) generally works on a resonance mode, and its permeability will vary sharply around this resonance frequency. Consequently, the performance of a MM will be largely degraded if it is designed using a traditional optimization technique without considering these inevitable imperfections in fabrications. In this regard, the robustness of a solution must be included in the design optimization of a MM, exacerbating additional computational burdens to the already costly design procedure arising from the application of a high-fidelity model. Consequently, it is demanding to reduce the function calls for a high-fidelity model in the robust design of a MM. In this point of view, an optimization methodology by combining the genetic algorithm (GA) and a local search surrogate model is proposed for efficiently robust optimizations of a MM unit.
Keywords: Genetic algorithm, metamaterial, robust optimization, surrogate model
