Abstract 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.

中文翻译：

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一种基于差分进化算法的波前整形全局优化新方法

摘要 本文提出了一种新的基于波前整形的聚焦方法，通过引入差分进化算法（DEA），与竞争对手算法相比，实现了更快的收敛速度和改进的增强。通过模拟，我们表明我们提出的基于 DEA 的方法可提供最佳的聚焦性能，而不受噪声的影响。实验结果表明，与传统的优化方法相比，DEA 提高了相同数量的测量的增强。此外，我们揭示了某些 DEA 参数的影响，导致出现了许多表现令人印象深刻的修改后的 DEA。提出的基于 DEA 的方法简化了波前整形的计算复杂度和实现过程，