混沌策略和非线性收敛因子的核参数寻优算法

Kernel Parameter Optimization Algorithm for Chaotic Strategy and Nonlinear Convergence Factor

为有效地解决SVM核参数寻优问题,提出了一种基于Fuch混沌策略协同非线性收敛因子的灰狼优化算法(FGWO)。在算法的3个阶段分别引入Fuch混沌反向学习策略、动态非线性控制参数、双权重因子策略和胜劣汰选择策略,为平衡全局探索和局部开发性能提供了新机制,增强了算法的收敛速度和收敛精度;以FGWO为新策略,构建一种FGWO-SVM分类模型,实现铝铸件表面缺陷识别。为验证算法的性能,引入10个标准测试函数,采用本文FGWO与其他算法相比较。结果表明,FGWO可以有效地解决函数优化问题;将FGWO-SVM模型应用于缺陷识别问题上,该模型对缺陷类型的平均识别率为96.6%,优于其他分类器。

In order to effectively solve the optimization problem of SVM kernel parameters,a gray wolf optimization algorithm(FGWO)based on Fuch chaos strategy and nonlinear convergence factor is proposed in this paper.This method introduces Fuch chaos reverse learning strategy,dynamic nonlinear control parameters,dual weight factor strategy and survival of the fittest selection strategy,respectively in the three stages of the algorithm.The purpose is to provide a new mechanism for balancing the performance of global exploration and local development,and to enhance the convergence speed and accuracy of the algorithm.Secondly,using FGWO as a new strategy,FGWO-SVM classification model is constructed to realize the identification of surface defects of aluminum castings.In order to verify the performance of the algorithm,on the basis of 10 standard test functions,the FGWO algorithmof this paper is compared with other algorithms.The results show that FGWO can effectively solve the function optimization problem,andwhenthe FGWO-SVM model is applied to the defect recognition problem,its average recognition rate of the defect type is 96.6%,which is better than other classifiers.