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Illumination correction model with sine-cosine algorithm to optimize gray wolf population for least-squares support vector regression
Color Research and Application ( IF 1.4 ) Pub Date : 2021-08-11 , DOI: 10.1002/col.22716
Junyi Yang 1 , Haitao Zhao 2 , Sheng Chen 1
Affiliation  

The color of an object appears different from its true color when illuminated with light sources of different hues. To solve this problem, this article proposes a combination algorithm (SCA-GWO-LSSVR) based on the sine-cosine algorithm (SCA) and the gray wolf optimization (GWO) algorithm to optimize the regression prediction model of the least-squares support vector regression (LSSVR) algorithm. The performance of the traditional LSSVR is significantly affected by the penalty parameter (gamma) and the sig2 kernel function parameter. The proposed method uses the improved GWO algorithm to search the population to find the best LSSVR parameter solution. The proposed algorithm uses the SCA to create multiple random candidate solutions in population initialization to avoid blind initialization of the GWO algorithm. In the process of iterative optimization, the SCA is infiltrated, and its sine-cosine wave mathematical model is used to quickly identify the best outward or inward position of the gray wolf. Finally, the LSSVR combines the optimal sig2 kernel function parameters and penalty parameters (gamma) to obtain a highly versatile illumination correction model. The experimental results show that the fitting accuracy of the proposed method reaches 86.8%, which is 5% higher than that of the LSSVR algorithm alone.

中文翻译:

使用正弦-余弦算法优化灰狼种群的光照校正模型,用于最小二乘支持向量回归

当用不同色调的光源照射时,物体的颜色看起来与其真实颜色不同。针对这个问题,本文提出了一种基于正余弦算法(SCA)和灰狼优化(GWO)算法的组合算法(SCA-GWO-LSSVR)来优化最小二乘支持向量的回归预测模型回归 (LSSVR) 算法。传统LSSVR的性能受惩罚参数(gamma)和sig2核函数参数的影响很大。所提出的方法使用改进的GWO算法来搜索种群以寻找最佳的LSSVR参数解。所提出的算法在种群初始化中使用SCA来创建多个随机候选解,以避免GWO算法的盲目初始化。在迭代优化的过程中,渗透了SCA,利用其正余弦波数学模型,快速识别出灰狼的最佳向外或向内位置。最后,LSSVR 结合了最优的 sig2 核函数参数和惩罚参数(gamma)来获得高度通用的光照校正模型。实验结果表明,所提方法的拟合精度达到了86.8%,比单独使用LSSVR算法提高了5个百分点。
更新日期:2021-08-11
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