{"title":"遗传算法与粒子群算法求解多任务调度问题的比较研究","authors":"Tian-jiao Zhang, Wen-lan Fan, Yanli Li","doi":"10.1109/ICNC.2009.206","DOIUrl":null,"url":null,"abstract":"Genetic algorithm and particle swarm optimization both belong to the evolutionary algorithms; they have much in common, but also have some differences. The paper set out from Multi-task scheduling problem, discussed in detail the method of utilizing GA and PSO to equilibrium and optimize Multi-task scheduling problem under the constraints of resources separately. Through the analysis of comparative experiment, two kinds of intelligence-optimizing methods made very good results when solved a same problem, but in most cases, PSO had a faster rate of convergence than GA, but GA had a better convergent result than PSO.","PeriodicalId":235382,"journal":{"name":"2009 Fifth International Conference on Natural Computation","volume":"3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"The Comparative Research of Solving Multi-task Scheduling Problems with GA and PSO\",\"authors\":\"Tian-jiao Zhang, Wen-lan Fan, Yanli Li\",\"doi\":\"10.1109/ICNC.2009.206\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Genetic algorithm and particle swarm optimization both belong to the evolutionary algorithms; they have much in common, but also have some differences. The paper set out from Multi-task scheduling problem, discussed in detail the method of utilizing GA and PSO to equilibrium and optimize Multi-task scheduling problem under the constraints of resources separately. Through the analysis of comparative experiment, two kinds of intelligence-optimizing methods made very good results when solved a same problem, but in most cases, PSO had a faster rate of convergence than GA, but GA had a better convergent result than PSO.\",\"PeriodicalId\":235382,\"journal\":{\"name\":\"2009 Fifth International Conference on Natural Computation\",\"volume\":\"3 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-08-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 Fifth International Conference on Natural Computation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICNC.2009.206\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 Fifth International Conference on Natural Computation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICNC.2009.206","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The Comparative Research of Solving Multi-task Scheduling Problems with GA and PSO
Genetic algorithm and particle swarm optimization both belong to the evolutionary algorithms; they have much in common, but also have some differences. The paper set out from Multi-task scheduling problem, discussed in detail the method of utilizing GA and PSO to equilibrium and optimize Multi-task scheduling problem under the constraints of resources separately. Through the analysis of comparative experiment, two kinds of intelligence-optimizing methods made very good results when solved a same problem, but in most cases, PSO had a faster rate of convergence than GA, but GA had a better convergent result than PSO.