Evolving regularised random vector functional link by seagull optimisation algorithm for yarn-dyed fabric colour difference classification

To address the issue of low precision in classifying the colour differences of yarn-dyed fabrics and the high cost of manual detection, a colour difference classification method relying on an improved seagull optimisation algorithm (SOA) optimised regularised random vector functional link (RRVFL) model is proposed for dyed fabrics. First, to address the issue of the slow convergence speed of the SOA, the current study optimises the initial SOA group with the marine predators algorithm (MPA) so that it can effectively improve the convergence ability and global optimisation ability of the SOA. Subsequently, the enhanced SOA is applied to fine-tune the parameters of the RRVFL. Compared with the methods that only optimise weights and bias, the proposed algorithm obtained by optimizing the initial group of SOA through the Marine Predators Algorithm (MSOA)-RRVFL model in this paper also increases the optimisation of the number of nodes in the hidden layer and regularisation parameters, which also effectively avoids the issue of the low classification accuracy of the RRVFL model due to random related parameters. Finally, by comparing the RRVFL model with other optimisation algorithms, the experimental outcomes demonstrate that the convergence ability of the improved SOA has been improved, and that the average accuracy of colour difference classification by the MSOA-RRVFL model is as high as 99.79%, and that the classification error fluctuation can be stabilised below 0.2%. In general, the MSOA-RRVFL model displays an excellent performance in terms of stability and significance.