{"title":"异步特定群优化","authors":"M. Ordukaya","doi":"10.1109/SIU.2007.4298807","DOIUrl":null,"url":null,"abstract":"In this paper.we present particle swarm optimization based on asynchronous algorithms for a multi-agent swarm.We study generalized PSO algoritm with two schemes called global best (gbest) and local best (lbest).We apply these two schemes to our multimodel function.Generally PSO is performed with synchronous algorithms.Our corparative study indicates that PSO modelled with asynchronous algorithm converges to a real desired value as synchronous models.","PeriodicalId":315147,"journal":{"name":"2007 IEEE 15th Signal Processing and Communications Applications","volume":"284 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Asynchronous Particular Swarm Optimization\",\"authors\":\"M. Ordukaya\",\"doi\":\"10.1109/SIU.2007.4298807\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper.we present particle swarm optimization based on asynchronous algorithms for a multi-agent swarm.We study generalized PSO algoritm with two schemes called global best (gbest) and local best (lbest).We apply these two schemes to our multimodel function.Generally PSO is performed with synchronous algorithms.Our corparative study indicates that PSO modelled with asynchronous algorithm converges to a real desired value as synchronous models.\",\"PeriodicalId\":315147,\"journal\":{\"name\":\"2007 IEEE 15th Signal Processing and Communications Applications\",\"volume\":\"284 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-06-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2007 IEEE 15th Signal Processing and Communications Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SIU.2007.4298807\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 IEEE 15th Signal Processing and Communications Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SIU.2007.4298807","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In this paper.we present particle swarm optimization based on asynchronous algorithms for a multi-agent swarm.We study generalized PSO algoritm with two schemes called global best (gbest) and local best (lbest).We apply these two schemes to our multimodel function.Generally PSO is performed with synchronous algorithms.Our corparative study indicates that PSO modelled with asynchronous algorithm converges to a real desired value as synchronous models.
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