Yi Zhao¹, Qunpo Liu^1,2, Hui Wang¹, Yuxi Zhao¹
1School of Electrical Engineering and Automation, Henan Polytechnic University, 2001 Century Avenue, Jiaozuo, (454003), Henan, China 2Henan International Joint Laboratory of Direct Drive and Control of Intelligent Equipment, Jiaozuo 454000, China
pp. 20–26
ABSTRACTAn 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.

ARTICLE INFO
Article History
Received 10 October 2022
Accepted 31 August 2023

Keywords
Apple external quality assessment
Enhanced grey wolf optimization arithmetic IGWO
Support vector machine
Reference function
IGWO-SVM

JAALR4104

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