Liu Kaige, Zhang Hengkang, Zhang Bin, Liu Qiang
Opt Express. 2021 May 10;29(10):15517-15527. doi: 10.1364/OE.424002.
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.
浑浊介质的散射效应会导致光波前畸变。利用波前整形技术可以实现光通过浑浊介质的聚焦。智能优化算法和神经网络算法是波前整形领域中两种强大的算法类型，但它们各有优缺点。在本文中，我们提出了一种新的混合算法，该算法将粒子群优化算法（PSO）和单层神经网络（SLNN）相结合，以实现两者的互补优势。使用少量训练集对SLNN进行训练以获得初步聚焦结果，之后PSO继续优化至全局最优。该混合算法比PSO实现了更快的收敛速度和更高的增强效果，同时减少了SLNN训练所需训练样本的数量。用1700个训练集训练的SLNN可以使PSO的收敛速度加快约50%，并使最终增强效果提高约24%。这种混合算法在生物医学和粒子操纵等领域将具有重要意义。
