波前
计算机科学
趋同(经济学)
算法
过程(计算)
差异进化
差速器(机械装置)
噪音(视频)
光学
人工智能
物理
图像(数学)
经济增长
热力学
操作系统
经济
作者
Yingzi Hua,Xiubao Sui,Shenghang Zhou,Qian Chen,Guohua Gu,Hongyang Bai,Wei Li
标识
DOI:10.1016/j.optcom.2020.126541
摘要
This paper proposes a novel wavefront-shaping-based focusing method, by introducing the differential evolution algorithm (DEA), thereby realising a faster convergence rate and improved enhancement compared to rival algorithms. Via simulations, we show that our proposed DEA-based approach delivers the best focusing performance irrespective of the influence of noise. Experimental results demonstrate that the DEA boosts the enhancement for an equivalent number of measurements compared with conventional optimisation methods. Furthermore, we reveal the influence of certain DEA parameters, leading to the emergence of many modified DEAs that perform impressively. The proposed DEA-based method simplifies the computational complexity and implementation process of wavefront shaping, offering useful insights for the future study of optimisation algorithms for wavefront shaping, as well as potential for practical applications, such as deep tissue focusing.
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