學術產出-Periodical Articles
Article View/Open
Publication Export
-
題名 Quality-Oriented Study on Mapping Island Model Genetic Algorithm onto CUDA GPU. 作者 周平
Chou, Ping
Sun, Xue
Wu*, Chao-Chin
Chen, Liang-Rui貢獻者 資管碩二 關鍵詞 genetic algorithm ; island model ; unequal area facility layout problem ; quality 日期 2019-03 上傳時間 6-Mar-2020 10:51:47 (UTC+8) 摘要 Genetic algorithm (GA), a global search method, has widespread applications in various fields. One very promising variant model of GA is the island model GA (IMGA) that introduces the key idea of migration to explore a wider search space. Migration will exchange chromosomes between islands, resulting in better-quality solutions. However, IMGA takes a long time to solve the large-scale NP-hard problems. In order to shorten the computation time, modern graphic process unit (GPU), as highly-parallel architecture, has been widely adopted in order to accelerate the execution of NP-hard algorithms. However, most previous studies on GPUs are focused on performance only, because the found solution qualities of the CPU and the GPU implementation of the same method are exactly the same. Therefore, it is usually previous work that did not report on quality. In this paper, we investigate how to find a better solution within a reasonable time when parallelizing IMGA on GPU, and we take the UA-FLP as a study example. Firstly, we propose an efficient approach of parallel tournament selection operator on GPU to achieve a better solution quality in a shorter amount of time. Secondly, we focus on how to tune three important parameters of IMGA to obtain a better solution efficiently, including the number of islands, the number of generations, and the number of chromosomes. In particular, different parameters have a different impact on solution quality improvement and execution time increment. We address the challenge of how to trade off between solution quality and execution time for these parameters. Finally, experiments and statistics are conducted to help researchers set parameters more efficiently to obtain better solutions when GPUs are used to accelerate IMGA. It has been observed that the order of influence on solution quality is: The number of chromosomes, the number of generations, and the number of islands, which can guide users to obtain better solutions efficiently with moderate increment of execution time. Furthermore, if we give higher priority on reducing execution time on GPU, the quality of the best solution can be improved by about 3%, with an acceleration that is 29 times faster than the CPU counterpart, after applying our suggested parameter settings. However, if we give solution quality a higher priority, i.e., the GPU execution time is close to the CPU’s, the solution quality can be improved up to 8%. 關聯 Symmetry, Vol.11, No.3, pp.318 資料類型 article DOI https://doi.org/10.3390/sym11030318 dc.contributor 資管碩二 dc.creator (作者) 周平 dc.creator (作者) Chou, Ping dc.creator (作者) Sun, Xue dc.creator (作者) Wu*, Chao-Chin dc.creator (作者) Chen, Liang-Rui dc.date (日期) 2019-03 dc.date.accessioned 6-Mar-2020 10:51:47 (UTC+8) - dc.date.available 6-Mar-2020 10:51:47 (UTC+8) - dc.date.issued (上傳時間) 6-Mar-2020 10:51:47 (UTC+8) - dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/129126 - dc.description.abstract (摘要) Genetic algorithm (GA), a global search method, has widespread applications in various fields. One very promising variant model of GA is the island model GA (IMGA) that introduces the key idea of migration to explore a wider search space. Migration will exchange chromosomes between islands, resulting in better-quality solutions. However, IMGA takes a long time to solve the large-scale NP-hard problems. In order to shorten the computation time, modern graphic process unit (GPU), as highly-parallel architecture, has been widely adopted in order to accelerate the execution of NP-hard algorithms. However, most previous studies on GPUs are focused on performance only, because the found solution qualities of the CPU and the GPU implementation of the same method are exactly the same. Therefore, it is usually previous work that did not report on quality. In this paper, we investigate how to find a better solution within a reasonable time when parallelizing IMGA on GPU, and we take the UA-FLP as a study example. Firstly, we propose an efficient approach of parallel tournament selection operator on GPU to achieve a better solution quality in a shorter amount of time. Secondly, we focus on how to tune three important parameters of IMGA to obtain a better solution efficiently, including the number of islands, the number of generations, and the number of chromosomes. In particular, different parameters have a different impact on solution quality improvement and execution time increment. We address the challenge of how to trade off between solution quality and execution time for these parameters. Finally, experiments and statistics are conducted to help researchers set parameters more efficiently to obtain better solutions when GPUs are used to accelerate IMGA. It has been observed that the order of influence on solution quality is: The number of chromosomes, the number of generations, and the number of islands, which can guide users to obtain better solutions efficiently with moderate increment of execution time. Furthermore, if we give higher priority on reducing execution time on GPU, the quality of the best solution can be improved by about 3%, with an acceleration that is 29 times faster than the CPU counterpart, after applying our suggested parameter settings. However, if we give solution quality a higher priority, i.e., the GPU execution time is close to the CPU’s, the solution quality can be improved up to 8%. dc.format.extent 3751117 bytes - dc.format.mimetype application/pdf - dc.relation (關聯) Symmetry, Vol.11, No.3, pp.318 dc.subject (關鍵詞) genetic algorithm ; island model ; unequal area facility layout problem ; quality dc.title (題名) Quality-Oriented Study on Mapping Island Model Genetic Algorithm onto CUDA GPU. dc.type (資料類型) article dc.identifier.doi (DOI) 10.3390/sym11030318 dc.doi.uri (DOI) https://doi.org/10.3390/sym11030318
