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Deep learning has been widely used in different fields such as computer vision and speech processing. The performance of deep learning algorithms is greatly affected by their hyperparameters. For complex machine learning models such as deep neural networks, it is difficult to determine their hyperparameters. In addition, existing hyperparameter optimization algorithms easily converge to a local optimal solution. This paper proposes a method for hyperparameter optimization that combines the Sparrow Search Algorithm and Particle Swarm Optimization, called the Hybrid Sparrow Search Algorithm. This method takes advantages of avoiding the local optimal solution in the Sparrow Search Algorithm and the search efficiency of Particle Swarm Optimization to achieve global optimization. Experiments verified the proposed algorithm in simple and complex networks. The results show that the Hybrid Sparrow Search Algorithm has the strong global search capability to avoid local optimal solutions and satisfactory search efficiency in both low and high-dimensional spaces. The proposed method provides a new solution for hyperparameter optimization problems in deep learning models. [ABSTRACT FROM AUTHOR]

A support vector machine (SVM) is an important method in the detection and classification of the color difference on a polysilicon wafer. However, the accuracy of a SVM is affected by its feature vector and parameters. Owing to the complex color information and random texture features on the wafer surface, the feature design is extremely complicated. Meanwhile, a SVM optimized using a popular intelligent algorithm easily falls into a local optimum, and the convergence of the algorithm needs to be improved. Therefore, a classification method is proposed for detecting the color difference from multi-scale features in polysilicon wafer images. First, to extract the features, an image segmentation method is devised based on the maximum region contrast, which effectively applies a threshold segmentation of the wafer images. Second, the multi-scale features and color representations in different color spaces are used to construct a nine-dimensional feature vector that sufficiently describes the surface characteristics of the wafer. An approach to optimize the SVM is finally proposed using a magnetic bacteria optimization algorithm based on an elitist strategy for parameter optimization. The optimum individual of each generation is used to adjust the magnetic moment such that the solution approaches the optimal direction and enhances the global search ability. A fitness function is also introduced to improve the diversity of the solutions through a cross-validation method. The experiment results show that the proposed algorithm achieves an accuracy of 98.3% with a better classification performance than the other methods and that the color difference of polysilicon wafers can be effectively detected. [ABSTRACT FROM AUTHOR]