Journal
TEXTILE RESEARCH JOURNAL
Volume 90, Issue 2, Pages 135-155Publisher
SAGE PUBLICATIONS LTD
DOI: 10.1177/0040517519859933
Keywords
color difference classification; differential evolution; extreme learning machine; whale optimization algorithm
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Funding
- NSFC-Zhejiang Joint Fund for the Integration of Industrialization and Informatization [U1609205]
- Zhejiang Provincial Natural Science Foundation of China [LY18F030018]
- Science Foundation of Zhejiang Sci-Tech University [18032232-Y]
- Zhejiang Top Priority Discipline of Textile Science and Engineering
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To mitigate the problem of low classification accuracy in solid color printing and dyeing, a color difference classification model based on the differential evolution (DE) improved whale optimization algorithm (WOA) for extreme learning machine (ELM) optimization, named the DE-WOA-ELM, was developed in this study. Considering that the initial population of the WOA has a significant influence on the solution speed and quality, DE was used to generate a more suitable initial population for the WOA by avoiding local optima, thereby improving the performance. The method used an excellent global search ability to improve the WOA for optimization and obtained an optimal parameter combination for the ELM. Thus, the problem of randomly initializing the input weight and the hidden layer bias of the ELM, which leads to a nonuniform training model and unstable algorithm, was solved. Finally, by optimizing the input weight and hidden layer bias, the color difference classification model of the ELM with a strong generalization ability was constructed. The results of the color difference classification experiments on fabric images collected under standard light sources show that the average classification accuracy for the dataset is increased by 2.15%, 11.06%, 12.11%, and 0.47% compared with those of the ELM, support vector machine, back propagation neural network, and kernel ELM, respectively.
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