A Fast Opposition-Based Differential Evolution with Cauchy Mutation

Yong Wu, Bin Zhao, Jinglei Guo
{"title":"A Fast Opposition-Based Differential Evolution with Cauchy Mutation","authors":"Yong Wu, Bin Zhao, Jinglei Guo","doi":"10.1109/GCIS.2012.91","DOIUrl":null,"url":null,"abstract":"Opposition-based Differential Evolution (ODE) has been proved to be an effective method to Differential Evolution (DE) in solving many optimization functions, and it's faster and more robust convergence than classical DE. In this paper, a fast ODE algorithm (FODE), using a local search method with Cauchy mutation is proposed. The simulation experiments are conducted on a comprehensive set of 10 complex benchmark functions. Compared with ODE, FODE is faster and more robust.","PeriodicalId":337629,"journal":{"name":"2012 Third Global Congress on Intelligent Systems","volume":"38 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 Third Global Congress on Intelligent Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/GCIS.2012.91","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

Abstract

Opposition-based Differential Evolution (ODE) has been proved to be an effective method to Differential Evolution (DE) in solving many optimization functions, and it's faster and more robust convergence than classical DE. In this paper, a fast ODE algorithm (FODE), using a local search method with Cauchy mutation is proposed. The simulation experiments are conducted on a comprehensive set of 10 complex benchmark functions. Compared with ODE, FODE is faster and more robust.
基于对立的柯西突变快速差分进化
基于对立的差分进化算法(ODE)已被证明是差分进化算法求解许多优化函数的一种有效方法,它比经典的差分进化算法具有更快和更强的收敛性。本文提出了一种基于Cauchy突变的局部搜索方法的快速差分进化算法(FODE)。在10个复杂基准函数的综合集上进行了仿真实验。与ODE相比,FODE速度更快,鲁棒性更强。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信