稳健性(进化)
超材料
优化设计
功能(生物学)
功勋
数学优化
标准差
计算机科学
实验设计
工程类
电子工程
数学
材料科学
基因
机器学习
化学
生物化学
光电子学
生物
计算机视觉
统计
进化生物学
作者
Yiying Li,Dun Sun,Shiyou Yang
出处
期刊:Compel-the International Journal for Computation and Mathematics in Electrical and Electronic Engineering
[Emerald Publishing Limited]
日期:2022-05-18
卷期号:42 (1): 14-25
标识
DOI:10.1108/compel-01-2022-0034
摘要
Purpose The purpose of this paper is to develop a robust optimization methodology for metamaterial (MM) unit designs to minimize the effect of manufacturing and operational uncertainties. Design/methodology/approach A new robustness quantification function, applicable to both convex and nonconvex relationships between the mean and the standard deviation, is introduced. A distance-based local radial basis function network surrogate model is proposed to substitute the global radial basis function network to reduce the heavy computational cost without any scarification on the solution accuracy. Findings The optimized results of a prototype MM unit demonstrate the feasibility and merit of the proposed methodology. The proposed methodology outperforms the existing ones in both performance and robust parameters in the design of a prototype MM unit. Originality/value It provides a robust optimization methodology for MM units when considering the imperfections in fabrications and fluctuations in operation and environment conditions in engineering applications.
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