{"title":"基于混合遗传算法的异构多处理器系统实时任务调度","authors":"Myungryun, Yoo","doi":"10.17265/1548-7709/2016.03.001","DOIUrl":null,"url":null,"abstract":"The real-time multiprocessor scheduling problem is one of the NP-hard problems. Furthermore, there are no papers which are concerned to heterogeneous multiprocessors system. This paper proposes a new real-time task scheduling algorithm using hGA (hybrid genetic algorithm) on heterogeneous multiprocessor environment. In solution algorithms, the GA (genetic algorithm) and the SA (simulated annealing) are cooperatively used. In this method, the convergence of GA is improved by introducing the probability of SA as the criterion for acceptance of new trial solution. The objective of proposed scheduling algorithm is to minimize total tardiness. The effectiveness of the proposed algorithm is shown through simulation studies. In simulation studies, the results of proposed algorithm show better than that of other algorithms.","PeriodicalId":69156,"journal":{"name":"通讯和计算机:中英文版","volume":"13 1","pages":"103-115"},"PeriodicalIF":0.0000,"publicationDate":"2016-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Real-time Task Scheduling in Heterogeneous Multiprocessors System Using Hybrid Genetic Algorithm\",\"authors\":\"Myungryun, Yoo\",\"doi\":\"10.17265/1548-7709/2016.03.001\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The real-time multiprocessor scheduling problem is one of the NP-hard problems. Furthermore, there are no papers which are concerned to heterogeneous multiprocessors system. This paper proposes a new real-time task scheduling algorithm using hGA (hybrid genetic algorithm) on heterogeneous multiprocessor environment. In solution algorithms, the GA (genetic algorithm) and the SA (simulated annealing) are cooperatively used. In this method, the convergence of GA is improved by introducing the probability of SA as the criterion for acceptance of new trial solution. The objective of proposed scheduling algorithm is to minimize total tardiness. The effectiveness of the proposed algorithm is shown through simulation studies. In simulation studies, the results of proposed algorithm show better than that of other algorithms.\",\"PeriodicalId\":69156,\"journal\":{\"name\":\"通讯和计算机:中英文版\",\"volume\":\"13 1\",\"pages\":\"103-115\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-03-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"通讯和计算机:中英文版\",\"FirstCategoryId\":\"1093\",\"ListUrlMain\":\"https://doi.org/10.17265/1548-7709/2016.03.001\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"通讯和计算机:中英文版","FirstCategoryId":"1093","ListUrlMain":"https://doi.org/10.17265/1548-7709/2016.03.001","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Real-time Task Scheduling in Heterogeneous Multiprocessors System Using Hybrid Genetic Algorithm
The real-time multiprocessor scheduling problem is one of the NP-hard problems. Furthermore, there are no papers which are concerned to heterogeneous multiprocessors system. This paper proposes a new real-time task scheduling algorithm using hGA (hybrid genetic algorithm) on heterogeneous multiprocessor environment. In solution algorithms, the GA (genetic algorithm) and the SA (simulated annealing) are cooperatively used. In this method, the convergence of GA is improved by introducing the probability of SA as the criterion for acceptance of new trial solution. The objective of proposed scheduling algorithm is to minimize total tardiness. The effectiveness of the proposed algorithm is shown through simulation studies. In simulation studies, the results of proposed algorithm show better than that of other algorithms.