{"title":"改进的粒子群优化算法","authors":"Zhou Han-chang","doi":"10.1109/iccsnt.2011.6182027","DOIUrl":null,"url":null,"abstract":"To improve full searching ability, local searching ability, convergence rate and calculating precision of elementary particle swarm, based on classical PSO algorithm and quanta theory, an improved PSO algorithm with quantum behavior--cQPSO algorithm is proposed. Identical particle system is introduced to update the position of particle and chaos thought is introduced to chaotic search every particle, accordingly improving the full searching ability, local searching ability, convergence rate and calculating precision of elementary particle swarm. The experimental results of classical function show that capability of improved algorithm is superior to classical PSO algorithm and PSO algorithm with quantum behavior.","PeriodicalId":365328,"journal":{"name":"Computer Engineering and Design","volume":"28 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Improved particle swarm optimization algorithm\",\"authors\":\"Zhou Han-chang\",\"doi\":\"10.1109/iccsnt.2011.6182027\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"To improve full searching ability, local searching ability, convergence rate and calculating precision of elementary particle swarm, based on classical PSO algorithm and quanta theory, an improved PSO algorithm with quantum behavior--cQPSO algorithm is proposed. Identical particle system is introduced to update the position of particle and chaos thought is introduced to chaotic search every particle, accordingly improving the full searching ability, local searching ability, convergence rate and calculating precision of elementary particle swarm. The experimental results of classical function show that capability of improved algorithm is superior to classical PSO algorithm and PSO algorithm with quantum behavior.\",\"PeriodicalId\":365328,\"journal\":{\"name\":\"Computer Engineering and Design\",\"volume\":\"28 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computer Engineering and Design\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/iccsnt.2011.6182027\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer Engineering and Design","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/iccsnt.2011.6182027","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
To improve full searching ability, local searching ability, convergence rate and calculating precision of elementary particle swarm, based on classical PSO algorithm and quanta theory, an improved PSO algorithm with quantum behavior--cQPSO algorithm is proposed. Identical particle system is introduced to update the position of particle and chaos thought is introduced to chaotic search every particle, accordingly improving the full searching ability, local searching ability, convergence rate and calculating precision of elementary particle swarm. The experimental results of classical function show that capability of improved algorithm is superior to classical PSO algorithm and PSO algorithm with quantum behavior.