Wen Shuhua, Zhang Xueliang, Liu Hainan, Liu Shuyang, Wang Jiaying
{"title":"一种改进的粒子群算法","authors":"Wen Shuhua, Zhang Xueliang, Liu Hainan, Liu Shuyang, Wang Jiaying","doi":"10.1109/ICMLC.2005.1527455","DOIUrl":null,"url":null,"abstract":"A modified particle swarm optimization (MPSO) algorithm is presented based on the variance of the population's fitness. During computing, the inertia weight of MPSO is determined adaptively and randomly according to the variance of the populations fitness. And the ability of , particle swarm optimization algorithm (PSO) to break away from the local optimum is greatly improved. The simulating results show that this algorithm not only has great advantage of convergence property over standard simple PSO, but also can avoid the premature convergence problem effectively","PeriodicalId":145719,"journal":{"name":"2005 International Conference on Neural Networks and Brain","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2005-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"161","resultStr":"{\"title\":\"A Modified Particle Swarm Optimization Algorithm\",\"authors\":\"Wen Shuhua, Zhang Xueliang, Liu Hainan, Liu Shuyang, Wang Jiaying\",\"doi\":\"10.1109/ICMLC.2005.1527455\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A modified particle swarm optimization (MPSO) algorithm is presented based on the variance of the population's fitness. During computing, the inertia weight of MPSO is determined adaptively and randomly according to the variance of the populations fitness. And the ability of , particle swarm optimization algorithm (PSO) to break away from the local optimum is greatly improved. The simulating results show that this algorithm not only has great advantage of convergence property over standard simple PSO, but also can avoid the premature convergence problem effectively\",\"PeriodicalId\":145719,\"journal\":{\"name\":\"2005 International Conference on Neural Networks and Brain\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2005-11-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"161\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2005 International Conference on Neural Networks and Brain\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICMLC.2005.1527455\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2005 International Conference on Neural Networks and Brain","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMLC.2005.1527455","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A modified particle swarm optimization (MPSO) algorithm is presented based on the variance of the population's fitness. During computing, the inertia weight of MPSO is determined adaptively and randomly according to the variance of the populations fitness. And the ability of , particle swarm optimization algorithm (PSO) to break away from the local optimum is greatly improved. The simulating results show that this algorithm not only has great advantage of convergence property over standard simple PSO, but also can avoid the premature convergence problem effectively