{"title":"一种改进的动态自适应粒子群优化算法","authors":"Yang Bo, Zhang Ding-xue, Liao Rui-quan","doi":"10.1109/IIH-MSP.2007.32","DOIUrl":null,"url":null,"abstract":"To overcome premature searching by standard particle swarm optimization (PSO) algorithm for the large lost in population diversity, the measure of population diversity and its calculation are given, and an adaptive PSO with dynamically changing inertia weight is proposed. Simulation results show that the adaptive PSO not only effectively alleviates the problem of premature convergence, but also has fast convergence speed for balancing the trade-off between exploration and exploitation.","PeriodicalId":385132,"journal":{"name":"Third International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP 2007)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"A Modified Particle Swarm Optimization Algorithm with Dynamic Adaptive\",\"authors\":\"Yang Bo, Zhang Ding-xue, Liao Rui-quan\",\"doi\":\"10.1109/IIH-MSP.2007.32\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"To overcome premature searching by standard particle swarm optimization (PSO) algorithm for the large lost in population diversity, the measure of population diversity and its calculation are given, and an adaptive PSO with dynamically changing inertia weight is proposed. Simulation results show that the adaptive PSO not only effectively alleviates the problem of premature convergence, but also has fast convergence speed for balancing the trade-off between exploration and exploitation.\",\"PeriodicalId\":385132,\"journal\":{\"name\":\"Third International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP 2007)\",\"volume\":\"90 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-11-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Third International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP 2007)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IIH-MSP.2007.32\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Third International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP 2007)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IIH-MSP.2007.32","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Modified Particle Swarm Optimization Algorithm with Dynamic Adaptive
To overcome premature searching by standard particle swarm optimization (PSO) algorithm for the large lost in population diversity, the measure of population diversity and its calculation are given, and an adaptive PSO with dynamically changing inertia weight is proposed. Simulation results show that the adaptive PSO not only effectively alleviates the problem of premature convergence, but also has fast convergence speed for balancing the trade-off between exploration and exploitation.
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