{"title":"基于改进差分进化方法的电磁器件优化","authors":"L. dos Santos Coelho, P. Alotto","doi":"10.1109/CEFC-06.2006.1633085","DOIUrl":null,"url":null,"abstract":"Recently, evolutionary algorithms (e.g. genetic algorithms, evolutionary programming, and evolution strategies) have proven to be useful tools for the optimization of difficult problems in electromagnetics. Differential evolution (DE) is one comparatively simple variant of an evolutionary algorithm using floating-point encoding and few control parameters. This work presents improved DE algorithms based on linearly time varying control parameters, sinusoidal functions, and diversity analysis of population","PeriodicalId":262549,"journal":{"name":"2006 12th Biennial IEEE Conference on Electromagnetic Field Computation","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Electromagnetic Device Optimization using Improved Differential Evolution Methods\",\"authors\":\"L. dos Santos Coelho, P. Alotto\",\"doi\":\"10.1109/CEFC-06.2006.1633085\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Recently, evolutionary algorithms (e.g. genetic algorithms, evolutionary programming, and evolution strategies) have proven to be useful tools for the optimization of difficult problems in electromagnetics. Differential evolution (DE) is one comparatively simple variant of an evolutionary algorithm using floating-point encoding and few control parameters. This work presents improved DE algorithms based on linearly time varying control parameters, sinusoidal functions, and diversity analysis of population\",\"PeriodicalId\":262549,\"journal\":{\"name\":\"2006 12th Biennial IEEE Conference on Electromagnetic Field Computation\",\"volume\":\"14 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-06-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2006 12th Biennial IEEE Conference on Electromagnetic Field Computation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CEFC-06.2006.1633085\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 12th Biennial IEEE Conference on Electromagnetic Field Computation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CEFC-06.2006.1633085","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Electromagnetic Device Optimization using Improved Differential Evolution Methods
Recently, evolutionary algorithms (e.g. genetic algorithms, evolutionary programming, and evolution strategies) have proven to be useful tools for the optimization of difficult problems in electromagnetics. Differential evolution (DE) is one comparatively simple variant of an evolutionary algorithm using floating-point encoding and few control parameters. This work presents improved DE algorithms based on linearly time varying control parameters, sinusoidal functions, and diversity analysis of population
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
