{"title":"一种带有动量因子的粒子群优化算法","authors":"Jinxia Ren, Shuai Yang","doi":"10.1109/ISCID.2011.13","DOIUrl":null,"url":null,"abstract":"The basic particle swarm optimization algorithm updated particles velocity only by the current particles velocity, the personal best position and the excellent particle position. Considering the influence of the previous changes among the current particles velocity, in this paper, the updating formula of particles velocity was mended by appending momentum factor, an improved particle swarm optimization algorithm with momentum factor was proposed. The simulation results show that the improved algorithm has higher accuracy and quicker convergence velocity than the basic particle swarm optimization algorithm.","PeriodicalId":224504,"journal":{"name":"2011 Fourth International Symposium on Computational Intelligence and Design","volume":"207 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"A Particle Swarm Optimization Algorithm with Momentum Factor\",\"authors\":\"Jinxia Ren, Shuai Yang\",\"doi\":\"10.1109/ISCID.2011.13\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The basic particle swarm optimization algorithm updated particles velocity only by the current particles velocity, the personal best position and the excellent particle position. Considering the influence of the previous changes among the current particles velocity, in this paper, the updating formula of particles velocity was mended by appending momentum factor, an improved particle swarm optimization algorithm with momentum factor was proposed. The simulation results show that the improved algorithm has higher accuracy and quicker convergence velocity than the basic particle swarm optimization algorithm.\",\"PeriodicalId\":224504,\"journal\":{\"name\":\"2011 Fourth International Symposium on Computational Intelligence and Design\",\"volume\":\"207 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-10-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 Fourth International Symposium on Computational Intelligence and Design\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISCID.2011.13\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 Fourth International Symposium on Computational Intelligence and Design","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISCID.2011.13","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Particle Swarm Optimization Algorithm with Momentum Factor
The basic particle swarm optimization algorithm updated particles velocity only by the current particles velocity, the personal best position and the excellent particle position. Considering the influence of the previous changes among the current particles velocity, in this paper, the updating formula of particles velocity was mended by appending momentum factor, an improved particle swarm optimization algorithm with momentum factor was proposed. The simulation results show that the improved algorithm has higher accuracy and quicker convergence velocity than the basic particle swarm optimization algorithm.
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