{"title":"多类数据集并行分类的粒子群优化实现","authors":"M. Balasaraswathi, B. Kalpana","doi":"10.1109/ICCCSP.2017.7944088","DOIUrl":null,"url":null,"abstract":"This research concept deals with Parallelizes PSO-SA model which combines the particle swarm optimization (PSO) and Simulated Annealing (SA). Parallel PSO-SA operates by parallelizing the operation of each of the particles and Multistart PSO runs parallel several normal versions of PSO embedded with Simulated Annealing in parallel. The experimental results were conducted on benchmark data sets and the proposed approach can reduce the time complexity and also to increase classification accuracy.","PeriodicalId":269595,"journal":{"name":"2017 International Conference on Computer, Communication and Signal Processing (ICCCSP)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Particle swarm optimization parallelism implementation to classify multiclass datasets\",\"authors\":\"M. Balasaraswathi, B. Kalpana\",\"doi\":\"10.1109/ICCCSP.2017.7944088\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This research concept deals with Parallelizes PSO-SA model which combines the particle swarm optimization (PSO) and Simulated Annealing (SA). Parallel PSO-SA operates by parallelizing the operation of each of the particles and Multistart PSO runs parallel several normal versions of PSO embedded with Simulated Annealing in parallel. The experimental results were conducted on benchmark data sets and the proposed approach can reduce the time complexity and also to increase classification accuracy.\",\"PeriodicalId\":269595,\"journal\":{\"name\":\"2017 International Conference on Computer, Communication and Signal Processing (ICCCSP)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 International Conference on Computer, Communication and Signal Processing (ICCCSP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCCSP.2017.7944088\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 International Conference on Computer, Communication and Signal Processing (ICCCSP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCCSP.2017.7944088","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Particle swarm optimization parallelism implementation to classify multiclass datasets
This research concept deals with Parallelizes PSO-SA model which combines the particle swarm optimization (PSO) and Simulated Annealing (SA). Parallel PSO-SA operates by parallelizing the operation of each of the particles and Multistart PSO runs parallel several normal versions of PSO embedded with Simulated Annealing in parallel. The experimental results were conducted on benchmark data sets and the proposed approach can reduce the time complexity and also to increase classification accuracy.
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