{"title":"一个多智能体粒子群优化框架及其应用","authors":"Xiyu Liu, Ke Xu, Hong Liu","doi":"10.1109/SPCA.2006.297580","DOIUrl":null,"url":null,"abstract":"The traditional particle swarm optimization technique is incorporated with multi-agent systems. A new PSO framework HMAS is presented with particles as agents. Actions and properties of agents are defined. We also present a test application in cluster analysis which extends the powerful algorithm CLARA and CLARANS. Implementation is given with experiment data. The results show that the new HMAS has better performance in searching the sample space than the non-agent systems","PeriodicalId":232800,"journal":{"name":"2006 First International Symposium on Pervasive Computing and Applications","volume":"25 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"A Multi-Agent Particle Swarm Optimization Framework with Applications\",\"authors\":\"Xiyu Liu, Ke Xu, Hong Liu\",\"doi\":\"10.1109/SPCA.2006.297580\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The traditional particle swarm optimization technique is incorporated with multi-agent systems. A new PSO framework HMAS is presented with particles as agents. Actions and properties of agents are defined. We also present a test application in cluster analysis which extends the powerful algorithm CLARA and CLARANS. Implementation is given with experiment data. The results show that the new HMAS has better performance in searching the sample space than the non-agent systems\",\"PeriodicalId\":232800,\"journal\":{\"name\":\"2006 First International Symposium on Pervasive Computing and Applications\",\"volume\":\"25 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2006 First International Symposium on Pervasive Computing and Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SPCA.2006.297580\",\"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 First International Symposium on Pervasive Computing and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SPCA.2006.297580","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Multi-Agent Particle Swarm Optimization Framework with Applications
The traditional particle swarm optimization technique is incorporated with multi-agent systems. A new PSO framework HMAS is presented with particles as agents. Actions and properties of agents are defined. We also present a test application in cluster analysis which extends the powerful algorithm CLARA and CLARANS. Implementation is given with experiment data. The results show that the new HMAS has better performance in searching the sample space than the non-agent systems
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