改进的灰狼优化算法在SVM参数优化中的应用

Application of improved GWO algorithm in SVM parameter optimization

为提升支持向量机(SVM)的性能,提出一种改进的灰狼优化(IGWO)算法并应用于SVM的参数优化。与传统的灰狼算法不同的是,改进算法采用非线性控制因子和随机权重位置更新策略。通过多个基准测试函数对IGWO与灰狼优化(GWO)算法进行性能比较,实验表明IGWO的性能优于GWO。最后建立IGWO-SVM分类模型,并应用到太阳能光伏故障诊断过程中,结果表明:该模型有效提升了故障诊断分类效率和分类识别率。

In order to improve the performance of support vector machine(SVM),an improved gray wolf optimization(IGWO) algorithm is proposed and applied to the parameter optimization of SVM.The improved algorithm is different from the traditional gray wolf algorithm, it uses nonlinear control factors and random weight position update strategy.Then make testes on multiple benchmark test functions to compare the performance of IGWO and GWO.The experiment shows that the performance of IGWO is better than GWO.Finally, an IGWO-SVM classification model is established and applied to the solar photovoltaic fault diagnosis process.The results show that the model effectively improves efficiency and recognition rate of the fault diagnosis classification.