H. Palit, Indar Sugiarto, D. Prayogo, Alexander T.K. Pratomo
{"title":"Performance Analysis of a Parallel Genetic Algorithm: A Case Study of the Traveling Salesman Problem","authors":"H. Palit, Indar Sugiarto, D. Prayogo, Alexander T.K. Pratomo","doi":"10.1109/ICISIT54091.2022.9872751","DOIUrl":null,"url":null,"abstract":"Genetic Algorithm (GA) is one of the most popular optimization techniques. Inspired by the theory of evolution and natural selection, it is also famous for its simplicity and versatility. Hence, it has been applied in diverse fields and domains. However, since it involves iterative and evolutionary processes, it takes a long time to obtain optimal solutions. To improve its performance, in this research work, we had parallelized GA processes to enable searching through the solution space with concurrent efforts. We had experimented with both CPU and GPU architectures. Speedups of GA solutions on CPU architecture range from 7.2 to 22.2, depending on the number of processing cores in the CPU. By contrast, speed-ups of GA solutions on GPU architecture can reach up to 172.4.","PeriodicalId":214014,"journal":{"name":"2022 1st International Conference on Information System & Information Technology (ICISIT)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 1st International Conference on Information System & Information Technology (ICISIT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICISIT54091.2022.9872751","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Genetic Algorithm (GA) is one of the most popular optimization techniques. Inspired by the theory of evolution and natural selection, it is also famous for its simplicity and versatility. Hence, it has been applied in diverse fields and domains. However, since it involves iterative and evolutionary processes, it takes a long time to obtain optimal solutions. To improve its performance, in this research work, we had parallelized GA processes to enable searching through the solution space with concurrent efforts. We had experimented with both CPU and GPU architectures. Speedups of GA solutions on CPU architecture range from 7.2 to 22.2, depending on the number of processing cores in the CPU. By contrast, speed-ups of GA solutions on GPU architecture can reach up to 172.4.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
