Color difference classification based on optimization support vector machine of improved grey wolf algorithm

In order to establish the color difference classification model of printing and dyeing products, a grey wolf algorithm optimization support vector machine based on differential evolution (DE) model is proposed in this paper. First of all, the performance of the support vector machine (SVM)model is mainly affected by the penalty parameter C and the RBF kernel width γ, and the method uses the good global search capability of grey wolf optimization (GWO) algorithm iteratively optimization to compute the best parameter combination of support vector machines. At the same time, because the initial population of grey wolf algorithm has a greater influence on the solution speed and quality of the algorithm, the DE algorithm is used to generate a more suitable initial population for grey wolf algorithm, which makes the grey wolf population have better solution ability. Finally, through the optimization to the penalty factor and the kernel width parameter, the printing and dyeing products classification model of SVM with strong generalization ability is constructed. The experimental results show that the proposed method achieves high classification accuracy, and have good stability and generalization ability, when it is compared with the color difference classification method of printing and dyeing product based on SVM and GWO-SVM algorithm.