{"title":"差分进化策略下的数据优化:最新研究综述","authors":"Tarik Eltaeib, J. Dichter","doi":"10.1109/ICPCSI.2017.8392102","DOIUrl":null,"url":null,"abstract":"In the optimization filed, there are various proposed algorithms and Differential Evolution (DE) is one of the most effective ones. Among the latter, there is need for more effective and efficient techniques, and strategies. Although most of these algorithms have demonstrated very good performance, but they still suffer from slow convergence rate. This paper reviews the DE, all its strategies, techniques, and some important algorithms.","PeriodicalId":6589,"journal":{"name":"2017 IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI)","volume":"48 1","pages":"17-23"},"PeriodicalIF":0.0000,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Data optimization with differential evolution strategies: A survey of the state-of-the-art\",\"authors\":\"Tarik Eltaeib, J. Dichter\",\"doi\":\"10.1109/ICPCSI.2017.8392102\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In the optimization filed, there are various proposed algorithms and Differential Evolution (DE) is one of the most effective ones. Among the latter, there is need for more effective and efficient techniques, and strategies. Although most of these algorithms have demonstrated very good performance, but they still suffer from slow convergence rate. This paper reviews the DE, all its strategies, techniques, and some important algorithms.\",\"PeriodicalId\":6589,\"journal\":{\"name\":\"2017 IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI)\",\"volume\":\"48 1\",\"pages\":\"17-23\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICPCSI.2017.8392102\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICPCSI.2017.8392102","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Data optimization with differential evolution strategies: A survey of the state-of-the-art
In the optimization filed, there are various proposed algorithms and Differential Evolution (DE) is one of the most effective ones. Among the latter, there is need for more effective and efficient techniques, and strategies. Although most of these algorithms have demonstrated very good performance, but they still suffer from slow convergence rate. This paper reviews the DE, all its strategies, techniques, and some important algorithms.
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