The scattering effect of turbid media can lead to optical wavefront distortion. Focusing light through turbid media can be achieved using wavefront shaping techniques. Intelligent optimization algorithms and neural network algorithms are two powerful types of algorithms in the field of wavefront shaping but have their advantages and disadvantages. In this paper, we propose a new hybrid algorithm that combines the particle swarm optimization algorithm (PSO) and single-layer neural network (SLNN) to achieve the complementary advantages of both. A small number of training sets are used to train the SLNN to obtain preliminary focusing results, after which the PSO continues to optimize to the global optimum. The hybrid algorithm achieves faster convergence and higher enhancement than the PSO, while reducing the size of training samples required for SLNN training. SLNN trained with 1700 training sets can speed up the convergence of the PSO by about 50% and boost the final enhancement by about 24%. This hybrid algorithm will be of great significance in fields such as biomedicine and particle manipulation.

中文翻译：

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基于神经网络的混合优化算法及其在波前整形中的应用

混浊介质的散射效应可能导致光波前畸变。使用波前整形技术可以使光通过混浊的介质聚焦。智能优化算法和神经网络算法是波阵面成形领域中两种强大的算法，但各有优缺点。在本文中，我们提出了一种新的混合算法，该算法结合了粒子群优化算法（PSO）和单层神经网络（SLNN）来实现两者的互补优势。少量训练集用于训练SLNN以获取初步的聚焦结果，然后PSO继续优化至全局最优。与PSO相比，混合算法可实现更快的收敛和更高的增强，同时减少了SLNN训练所需的训练样本的大小。使用1700套训练集训练的SLNN可以将PSO的收敛速度提高约50％，并将最终增强速度提高约24％。这种混合算法在诸如生物医学和粒子操纵等领域将具有重要意义。