基于灰狼优化算法的最小二乘支持向量机在软件老化预测中的应用

Application of least squares support vector machine based on grey wolf optimization algorithm in software aging prediction

预测模型参数的选取对其泛化能力和预测准确度,起着至关重要作用。基于径向基核函数的最小二乘支持向量机参数主要涉及惩罚因子和核函数参数,这两个参数的选择将直接影响最小二乘支持向量机的学习和泛化能力。为了提高最小二乘支持向量机的预测结果,文章用灰狼优化算法对其参数寻优,建立软件老化预测模型。通过实验证明了该模型,对软件老化的预测表现出很好的效果。

The selection of predictive model parameters plays a crucial role in its generalization ability and prediction accuracy.Least squares support vector machine parameters based on radial basis kernel functions mainly involve penalty factors and kernel function parameters.The choice of these two parameters will directly affect the learning and generalization ability of least squares support vector machines.In order to improve the prediction results of the least squares support vector machine,the paper uses the gray wolf optimization algorithm to optimize its parameters,and builds a software aging prediction model.The experiment proves that this model has a good effect on the prediction of software aging.