{"title":"基于对立的柯西突变快速差分进化","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":"{\"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}","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}
A Fast Opposition-Based Differential Evolution with Cauchy Mutation
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.
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