Classifying fabric wrinkle via regularized random vector function link optimized by African vultures optimization algorithm

Abstract

The wrinkling of fabrics not only affects their aesthetic appeal and comfort but also leads to increased wear and a reduced lifespan. Consequently, wrinkle resistance has become a critical metric in evaluating fabric performance. Traditional wrinkle grading methods rely heavily on expert assessments, which are susceptible to visual fatigue, resulting in subjective and inconsistent classification outcomes. To address this issue, this article proposes an automatic wrinkle grading model based on an improved African Vultures Optimization (AVO) algorithm, which optimizes a regularized random vector functional link (RRVFL) network. The model enhances convergence speed and classification accuracy by incorporating the Harris Hawks Optimization (HHO) algorithm to provide a superior initial population for the AVO algorithm. Additionally, the AVO algorithm’s local search capability is strengthened during the optimization process, improving the model’s stability and resistance to overfitting. Experimental results demonstrate that the proposed HHO-AVO-RRVFL model achieves an average classification accuracy of 97.86% across multiple datasets, significantly outperforming other comparative algorithms while also excelling in classification stability and convergence speed.