基于小波基函数的差分进化算法缩放因子改进方法及应用  

Scaling factor improvement method of differential evolution based on wavelet basis function and its application

作　　者：徐英杰 阎晓琳[2] 邓武[1] XU Ying-jie;YAN Xiao-lin;DENG Wu(College of Software,Dalian Jiaotong University,Dalian 116028,China;College of Mechanical Engineering,Dalian Jiaotong University,Dalian 116028,China)

机构地区：[1]大连交通大学软件学院,辽宁大连116028 [2]大连交通大学,机械工程学院,辽宁大连116028

出　　处：《云南民族大学学报（自然科学版）》2020年第1期33-38,共6页Journal of Yunnan Minzu University:Natural Sciences Edition

基　　金：国家自然科学基金(61771087,U1433124).

摘　　要：针对差分进化(DE)算法在求解复杂优化问题时存在收敛性和搜索能力差以及控制参数难以确定的问题,引入小波基函数,提出一种基于小波基函数的差分进化算法缩放因子改进方法.该方法采用小波基函数来改进DE缩放因子F,以保证解的多样性、加速算法收敛和提高算法性能.选择5个标准测试函数来测试改进DE算法的有效性,实验结果表明,改进的DE算法能有效解决控制参数难以确定的问题,提高了其搜索能力,获得了较好的最优解.Aiming at the problems of differential evolution algorithm(DE)in solving complex optimization problems,such as convergence and poor development ability and difficult to determine control parameters,a wavelet basis function is introduced and an improved differential evolution algorithm based on wavelet basis function is proposed.The wavelet basis function is used to scale the factor F to ensure the diversity of the solution,accelerate the convergence of the algorithm and improve the performance of the algorithm.The experimental results show that the improved algorithm can avoid the shortcomings of fixed parameter selection,and is superior to fixed parameter selection in optimization and convergence.

关 键 词：差分进化算法 小波基函数 缩放因子 标准测试函数 优化性能 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]