{"title":"自适应菌群优化","authors":"M. Muoz, J. Lopez, E. Caicedo","doi":"10.1109/CERMA.2008.97","DOIUrl":null,"url":null,"abstract":"This paper presents a self-adaptive bacteria swarm optimization algorithm, and its application in a suite of optimization benchmark problems, where the self-adaptive algorithm outperformed in most cases the non adaptive version. The algorithm follows a methodology that uses some concepts included in the evolution strategies for the parameter control, allowing the algorithm to select online the best parameter set.","PeriodicalId":126172,"journal":{"name":"2008 Electronics, Robotics and Automotive Mechanics Conference (CERMA '08)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Self–Adaptive Bacteria Swarm for Optimization\",\"authors\":\"M. Muoz, J. Lopez, E. Caicedo\",\"doi\":\"10.1109/CERMA.2008.97\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a self-adaptive bacteria swarm optimization algorithm, and its application in a suite of optimization benchmark problems, where the self-adaptive algorithm outperformed in most cases the non adaptive version. The algorithm follows a methodology that uses some concepts included in the evolution strategies for the parameter control, allowing the algorithm to select online the best parameter set.\",\"PeriodicalId\":126172,\"journal\":{\"name\":\"2008 Electronics, Robotics and Automotive Mechanics Conference (CERMA '08)\",\"volume\":\"39 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-09-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 Electronics, Robotics and Automotive Mechanics Conference (CERMA '08)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CERMA.2008.97\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 Electronics, Robotics and Automotive Mechanics Conference (CERMA '08)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CERMA.2008.97","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
This paper presents a self-adaptive bacteria swarm optimization algorithm, and its application in a suite of optimization benchmark problems, where the self-adaptive algorithm outperformed in most cases the non adaptive version. The algorithm follows a methodology that uses some concepts included in the evolution strategies for the parameter control, allowing the algorithm to select online the best parameter set.