{"title":"基于变异改进粒子群优化算法的聚焦爬虫模型","authors":"Guangxia Xu, Peng Jiang, Chuang Ma, M. Daneshmand","doi":"10.1109/ICII.2018.00031","DOIUrl":null,"url":null,"abstract":"The focused crawler is the key technology of the focused search engine. The current focused crawler is prone to poor adaptability and low search accuracy in the process of crawling the webpage. For these reasons, we proposes a focused crawler model (VRPSOFC) based on mutation improving particle swarm optimization. First, get three seed pages based on the click rate of the topic-related page. Then, get the four weighted documents of the seed pages. Finally, using the focused crawler model based on mutation improving particle swarm optimization algorithm to crawl the webpage, the results of the analysis show that the focused crawler model has a significant improvement in the precision of optimization.","PeriodicalId":330919,"journal":{"name":"2018 IEEE International Conference on Industrial Internet (ICII)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A Focused Crawler Model Based on Mutation Improving Particle Swarm Optimization Algorithm\",\"authors\":\"Guangxia Xu, Peng Jiang, Chuang Ma, M. Daneshmand\",\"doi\":\"10.1109/ICII.2018.00031\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The focused crawler is the key technology of the focused search engine. The current focused crawler is prone to poor adaptability and low search accuracy in the process of crawling the webpage. For these reasons, we proposes a focused crawler model (VRPSOFC) based on mutation improving particle swarm optimization. First, get three seed pages based on the click rate of the topic-related page. Then, get the four weighted documents of the seed pages. Finally, using the focused crawler model based on mutation improving particle swarm optimization algorithm to crawl the webpage, the results of the analysis show that the focused crawler model has a significant improvement in the precision of optimization.\",\"PeriodicalId\":330919,\"journal\":{\"name\":\"2018 IEEE International Conference on Industrial Internet (ICII)\",\"volume\":\"5 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE International Conference on Industrial Internet (ICII)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICII.2018.00031\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE International Conference on Industrial Internet (ICII)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICII.2018.00031","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Focused Crawler Model Based on Mutation Improving Particle Swarm Optimization Algorithm
The focused crawler is the key technology of the focused search engine. The current focused crawler is prone to poor adaptability and low search accuracy in the process of crawling the webpage. For these reasons, we proposes a focused crawler model (VRPSOFC) based on mutation improving particle swarm optimization. First, get three seed pages based on the click rate of the topic-related page. Then, get the four weighted documents of the seed pages. Finally, using the focused crawler model based on mutation improving particle swarm optimization algorithm to crawl the webpage, the results of the analysis show that the focused crawler model has a significant improvement in the precision of optimization.