{"title":"利用混合量子算法求解VRPTW","authors":"T. Ning, Chengzhi Guo","doi":"10.1109/ICICIP.2012.6391549","DOIUrl":null,"url":null,"abstract":"Proposed a novel optimal algorithm of hybrid quantum particle swarm optimization to solve VRPTW through combining QPSO with simulated annealing algorithm. The analysis of experimental data verified that the novel algorithm can improve the convergence reliability and speed within short time, and it is an effective solution for VRPTW.","PeriodicalId":376265,"journal":{"name":"2012 Third International Conference on Intelligent Control and Information Processing","volume":"16 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Using hybrid quantum algorithm to solve VRPTW\",\"authors\":\"T. Ning, Chengzhi Guo\",\"doi\":\"10.1109/ICICIP.2012.6391549\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Proposed a novel optimal algorithm of hybrid quantum particle swarm optimization to solve VRPTW through combining QPSO with simulated annealing algorithm. The analysis of experimental data verified that the novel algorithm can improve the convergence reliability and speed within short time, and it is an effective solution for VRPTW.\",\"PeriodicalId\":376265,\"journal\":{\"name\":\"2012 Third International Conference on Intelligent Control and Information Processing\",\"volume\":\"16 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-07-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 Third International Conference on Intelligent Control and Information Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICICIP.2012.6391549\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 Third International Conference on Intelligent Control and Information Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICICIP.2012.6391549","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Proposed a novel optimal algorithm of hybrid quantum particle swarm optimization to solve VRPTW through combining QPSO with simulated annealing algorithm. The analysis of experimental data verified that the novel algorithm can improve the convergence reliability and speed within short time, and it is an effective solution for VRPTW.