Conventional artificial color difference detection is error prone and inefficient. Herein, a novel color difference classification model is proposed for dyed fabrics via a kernel extreme learning machine based on an improved grasshopper optimization algorithm. First, in order to prevent the grasshopper optimization algorithm from succumbing to local optimality, it is proposed to optimize the initial population of the algorithm using differential evolution, resulting in a better solution direction at the outset. Then, this novel grasshopper algorithm is used to adjust the kernel bandwidth and penalty parameters of the learning maching model, thereby forming a color difference classification model for dyed fabrics based on the differential evolution grasshopper optimization algorithm kernel extreme learning machine. Finally, the key indicator values representing color difference are extracted from the printed and dyed product images collected under the standard light source. The color difference calculated by substituting these values into the color difference formula generates a color difference dataset, which is used to train and test the color difference classification model. Experimental results show that the proposed differential evolution grasshopper optimization algorithm kernel extreme learning machine model has high classification accuracy and impressive stability. The average classification accuracy of the proposed model is 98.89%, whereas the accuracy of kernel extreme learning machine is only 91.08%.

中文翻译：

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基于改进的蚱hopper优化算法的核极限学习机对染色织物的色差分类

常规的人工色差检测容易出错且效率低下。在此，基于改进的蚱hopper优化算法，通过核极限学习机提出了一种染色织物的色差分类模型。首先，为了防止蚱optimization优化算法陷入局部最优，提出了利用差分进化对算法的初始种群进行优化的方法，从而从一开始就产生了较好的求解方向。然后，将这种新颖的蚱hopper算法用于调整学习机器模型的内核带宽和惩罚参数，从而基于差分进化蚱hopper优化算法内核极限学习机，形成染色织物的色差分类模型。最后，从标准光源下收集的印染产品图像中提取代表色差的关键指标值。通过将这些值代入色差公式而计算出的色差将生成一个色差数据集，该数据集将用于训练和测试色差分类模型。实验结果表明，提出的差分进化蚱grass优化算法核极限学习机模型具有较高的分类精度和令人印象深刻的稳定性。该模型的平均分类精度为98.89％，而核极限学习机的分类精度仅为91.08％。通过将这些值代入色差公式而计算出的色差将生成一个色差数据集，该数据集将用于训练和测试色差分类模型。实验结果表明，提出的差分进化蚱grass优化算法核极限学习机模型具有较高的分类精度和令人印象深刻的稳定性。该模型的平均分类精度为98.89％，而核极限学习机的精度仅为91.08％。通过将这些值代入色差公式而计算出的色差将生成一个色差数据集，该数据集将用于训练和测试色差分类模型。实验结果表明，提出的差分进化蚱grass优化算法核极限学习机模型具有较高的分类精度和令人印象深刻的稳定性。该模型的平均分类精度为98.89％，而核极限学习机的精度仅为91.08％。实验结果表明，提出的差分进化蚱grass优化算法核极限学习机模型具有较高的分类精度和令人印象深刻的稳定性。该模型的平均分类精度为98.89％，而核极限学习机的精度仅为91.08％。实验结果表明，提出的差分进化蚱grass优化算法核极限学习机模型具有较高的分类精度和令人印象深刻的稳定性。该模型的平均分类精度为98.89％，而核极限学习机的精度仅为91.08％。