Variations in illumination lead to serious errors in the evaluation of chromatic aberration. We propose an illumination correction model based on the opposition‐based learning improved whale optimization algorithm for support vector regression optimization, named "OBL‐IWOA‐SVR." Because the initial population quality of the whale optimization algorithm has a significant impact on the solution speed and accuracy, the Opposition‐based learning strategy is adopted in this article to mix the original population and its opposite individuals and select the best as the new population, replacing the random initialization to generate a more suitable initial population. This increases the diversity of the population and thus overcomes the impact of the quality of the initial population on the algorithm performance. Secondly, the proposed algorithm adopts the adaptive weight and the convergence factor based on the variation of the cosine law to balance the algorithm's global exploration ability and local development ability and enhance the convergence accuracy. Finally, the algorithm utilizes good global searching ability to optimize the penalty factor and nuclear parameters and obtains the optimal support vector machine parameter combination to construct the illumination correction model OBL‐IWOA‐SVR accurately and robustly. Experimental results show that the illumination correction model proposed in this article is found superior to other models in significance analysis: the root mean square error of the proposed model is 0.0173, the smallest of all illumination correction models. Furthermore, the model exhibits good stability and high illumination estimation accuracy.

中文翻译：

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基于改进的鲸鱼优化算法的支持向量回归照明校正

照明的变化会导致色差评估中的严重错误。我们针对支持向量回归优化提出了一种基于对立面学习改进鲸鱼优化算法的照度校正模型，称为“ OBL-IWOA-SVR”。由于鲸鱼优化算法的初始种群质量会对求解速度和准确性产生重大影响，因此本文采用基于对立的学习策略来混合原始种群及其对立个体，并选择最佳种群作为新种群，替换随机初始化以生成更合适的初始填充。这增加了总体的多样性，因此克服了初始总体质量对算法性能的影响。其次，该算法基于余弦定律的变化，采用自适应权重和收敛因子，以平衡算法的全局探索能力和局部开发能力，提高收敛精度。最后，该算法利用良好的全局搜索能力来优化惩罚因子和核参数，并获得最优的支持向量机参数组合，以准确，鲁棒地构建照明校正模型OBL-IWOA-SVR。实验结果表明，在显着性分析中发现本文提出的照度校正模型优于其他模型：所提模型的均方根误差为0.0173，是所有照度校正模型中最小的。此外，