{"title":"先进Meta-PSO","authors":"Christian Veenhuis","doi":"10.1109/HIS.2006.10","DOIUrl":null,"url":null,"abstract":"One issue in applying PSO is to find a good working set of parameters. The standard settings are often work sufficiently but don¿t exhaust the possibilities of PSO. This paper proposes an extended Meta-PSO approach to optimize the PSO parameters as well as the neighborhood topology for a given problem by PSO itself. It is applied to four typical benchmark functions known from literature. The good results indicate that PSO is capable of optimizing itself.","PeriodicalId":150732,"journal":{"name":"2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06)","volume":"49 ","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Advanced Meta-PSO\",\"authors\":\"Christian Veenhuis\",\"doi\":\"10.1109/HIS.2006.10\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"One issue in applying PSO is to find a good working set of parameters. The standard settings are often work sufficiently but don¿t exhaust the possibilities of PSO. This paper proposes an extended Meta-PSO approach to optimize the PSO parameters as well as the neighborhood topology for a given problem by PSO itself. It is applied to four typical benchmark functions known from literature. The good results indicate that PSO is capable of optimizing itself.\",\"PeriodicalId\":150732,\"journal\":{\"name\":\"2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06)\",\"volume\":\"49 \",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-12-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/HIS.2006.10\",\"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 Sixth International Conference on Hybrid Intelligent Systems (HIS'06)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/HIS.2006.10","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
One issue in applying PSO is to find a good working set of parameters. The standard settings are often work sufficiently but don¿t exhaust the possibilities of PSO. This paper proposes an extended Meta-PSO approach to optimize the PSO parameters as well as the neighborhood topology for a given problem by PSO itself. It is applied to four typical benchmark functions known from literature. The good results indicate that PSO is capable of optimizing itself.
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