Design and Application of Enhanced Grey Wolf Optimization-based Support Vector Machine

Abstract

An enhanced variant of the Grey Wolf Optimization (GWO) algorithm, known as the Improved Grey Wolf Optimization (IGWO), was introduced with the primary objective of improving the precision of apple's external quality assessment categorization using Support Vector Machine (SVM) as the underlying classifier.The IGWO algorithm incorporates several enhancements, including the utilization of Logistic chaos mapping, a nonlinear convergence factor, and Cauchy variation. Initially, diverse benchmark functions were employed to assess the efficacy of the IGWO methodology. The experimental outcomes demonstrated that the IGWO method significantly enhanced both the rate of convergence and precision. Subsequently, an image processing approach was employed to capture the exogenous characteristics of apples, which were then utilized as the dataset. The IGWO method was employed to fine-tune the regularization parameters and kernel parameters in the SVM, resulting in the optimal IGWO-SVM classification model. Finally, a comparative analysis was conducted between the classification results obtained from SVM, GMO-SVM, and IGWO-SVM. The findings revealed that the IGWO-SVM model achieved the peak accurate classification performance, surpassing the other methods.