{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:33:21Z","timestamp":1760240001632,"version":"build-2065373602"},"reference-count":41,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2019,3,2]],"date-time":"2019-03-02T00:00:00Z","timestamp":1551484800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology, Taiwan","doi-asserted-by":"publisher","award":["MOST 106-2221-E-018-010"],"award-info":[{"award-number":["MOST 106-2221-E-018-010"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002888","name":"Beijing Municipal Commission of Education","doi-asserted-by":"publisher","award":["KM201811417010"],"award-info":[{"award-number":["KM201811417010"]}],"id":[{"id":"10.13039\/501100002888","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>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\u2019s, the solution quality can be improved up to 8%.<\/jats:p>","DOI":"10.3390\/sym11030318","type":"journal-article","created":{"date-parts":[[2019,3,4]],"date-time":"2019-03-04T05:45:36Z","timestamp":1551678336000},"page":"318","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Quality-Oriented Study on Mapping Island Model Genetic Algorithm onto CUDA GPU"],"prefix":"10.3390","volume":"11","author":[{"given":"Xue","family":"Sun","sequence":"first","affiliation":[{"name":"College of Urban Rail Transit and Logistics, Beijing Union University, Beijing 100101, China"},{"name":"Department of Electrical Engineering, National Changhua University of Education, Changhua 50007, Taiwan"}]},{"given":"Ping","family":"Chou","sequence":"additional","affiliation":[{"name":"Department of Management Information Systems, National Chengchi University, Taipei 11605, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0469-9707","authenticated-orcid":false,"given":"Chao-Chin","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Engineering, National Changhua University of Education, Changhua 50007, Taiwan"}]},{"given":"Liang-Rui","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, National Changhua University of Education, Changhua 50007, Taiwan"}]}],"member":"1968","published-online":{"date-parts":[[2019,3,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/j.compind.2004.06.003","article-title":"A solution to the unequal area facilities layout problem by genetic algorithm","volume":"56","author":"Wang","year":"2005","journal-title":"Comput. Ind."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.ejor.2008.06.028","article-title":"STaTS: A slicing tree and tabu search based heuristic for the unequal area facility layout problem","volume":"197","author":"Scholz","year":"2009","journal-title":"Eur. J. Oper. Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"5523","DOI":"10.1016\/j.eswa.2009.12.080","article-title":"Solving facility layout problems using Flexible Bay Structure representation and Ant System algorithm","volume":"37","author":"Wong","year":"2010","journal-title":"Expert Syst. Appl."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1877","DOI":"10.1080\/00207541003614371","article-title":"Unequal area flexible bay facility layout using ant colony optimisation","volume":"49","author":"Konak","year":"2011","journal-title":"Int. J. Prod. Res."},{"key":"ref_5","first-page":"259","article-title":"A combined zone-LP and simulated annealing algorithm for unequal-area facility layout problem","volume":"11","author":"Xiao","year":"2016","journal-title":"Adv. Prod. Eng. Manage."},{"key":"ref_6","unstructured":"Holland, J.H. (1975). Adaptation in Natural and Artificial Systems: An Introductory Analysis with Application to Biology, Control, and Artificial Intelligence, University of Michigan Press."},{"key":"ref_7","unstructured":"Kenneth, A.D.J. (1975). An analysis of the behavior of a class of genetic adaptive systems. [Ph.D. Thesis, University of Michigan]."},{"key":"ref_8","unstructured":"Darwin, C. (1962). The Origin of Species by Means of Natural Selection, or, the Preservation of Favoured Races in the Struggle for Life; with a Foreward by George Gaylord Simpson, Collier Books."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Khuda Bux, N., Lu, M., Wang, J., Hussain, S., and Aljeroudi, Y. (2018). Efficient association rules hiding using genetic algorithms. Symmetry, 10.","DOI":"10.3390\/sym10110576"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1023\/A:1022602019183","article-title":"Genetic algorithms and machine learning","volume":"3","author":"Goldberg","year":"1989","journal-title":"Mach. Learn."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.patcog.2016.01.012","article-title":"Human action recognition using genetic algorithms and convolutional neural networks","volume":"59","author":"Ijjina","year":"2016","journal-title":"Pattern Recognit."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1080\/00207179.2016.1230231","article-title":"Dynamic modelling and parameter estimation of a hydraulic robot manipulator using a multi-objective genetic algorithm","volume":"90","author":"Montazeri","year":"2017","journal-title":"Int. J. Control"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"384","DOI":"10.1016\/j.proeng.2016.07.497","article-title":"Optimal rehabilitation model for water pipeline systems with genetic algorithm","volume":"154","author":"Shin","year":"2016","journal-title":"Procedia Eng."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Deng, Q., Gong, G., Gong, X., Zhang, L., Liu, W., and Ren, Q. (2017). A bee evolutionary guiding nondominated sorting genetic Algorithm II for multiobjective flexible Job-shop scheduling. Comput. Intell. Neurosci., 2017.","DOI":"10.1155\/2017\/5232518"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1016\/j.jclepro.2017.02.096","article-title":"a genetic algorithm for reverse logistics network design: A case study from the GCC","volume":"151","author":"Alshamsi","year":"2017","journal-title":"J. Clean. Prod."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"04017011","DOI":"10.1061\/(ASCE)CP.1943-5487.0000653","article-title":"Site layout and construction plan optimization using an integrated genetic algorithm simulation framework","volume":"31","author":"RazaviAlavi","year":"2017","journal-title":"J. Comput. Civil Eng."},{"key":"ref_17","first-page":"59","article-title":"Implementation of a distributed genetic algorithm for parameter optimization in a cell nuclei detection project","volume":"10","year":"2013","journal-title":"Acta Polytech. Hung."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Sz\u00e9n\u00e1si, S., and Felde, I. (2017, January 26\u201328). Configuring genetic algorithm to solve the inverse heat conduction problem. Proceedings of the 2017 IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI 2017), Herlany, Slovakia.","DOI":"10.1109\/SAMI.2017.7880340"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Pospichal, P., Jaros, J., and Schwarz, J. (2010). Parallel Genetic Algorithm on the CUDA Architecture. Applications of Evolutionary Computation, Springer.","DOI":"10.1007\/978-3-642-12239-2_46"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/j.eswa.2016.10.004","article-title":"An island model genetic algorithm for unequal area facility layout problems","volume":"68","year":"2017","journal-title":"Expert Syst. Appl."},{"key":"ref_21","unstructured":"Starkweather, T., Mcdaniel, S., Whitley, D., Mathias, K., Whitley, D., and Dept, M.E. (1991, January 7\u201311). A comparison of genetic sequencing operators. Proceedings of the Fourth International Conference on Genetic Algorithms, San Diego, CA, USA."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1016\/0278-6125(96)84198-7","article-title":"The facility layout problem: Recent and emerging trends and perspectives","volume":"15","author":"Meller","year":"1996","journal-title":"J. Manuf. Syst."},{"key":"ref_23","unstructured":"Tompkins, J.A., White, J.A., Bozer, Y.A., and Tanchoco, J.M.A. (2010). Facilities Planning, John Wiley &Sons."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/0304-3975(76)90059-1","article-title":"Some simplified NP-complete graph problems","volume":"1","author":"Garey","year":"1976","journal-title":"Theor. Comput. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"713","DOI":"10.1007\/s12008-016-0362-z","article-title":"Modified genetic algorithms for solving facility layout problems","volume":"11","author":"Hasda","year":"2017","journal-title":"Int. J. Interact. Des. Manuf."},{"key":"ref_26","first-page":"787","article-title":"An elitist strategy genetic algorithm using simulated annealing algorithm as local search for facility layout design","volume":"84","author":"Leno","year":"2016","journal-title":"Int. J. Adv. Manuf. Tech."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"742","DOI":"10.1016\/j.ejor.2016.07.022","article-title":"A hybrid genetic algorithm with decomposition phases for the unequal area facility layout problem","volume":"256","author":"Paes","year":"2017","journal-title":"Eur. J. Oper. Res."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"799","DOI":"10.1080\/00207543.2015.1070217","article-title":"Unequal-area stochastic facility layout problems: Solutions using improved covariance matrix adaptation evolution strategy, particle swarm optimisation, and genetic algorithm","volume":"54","author":"Derakhshan","year":"2016","journal-title":"Int. J. Prod. Res."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1718","DOI":"10.1016\/j.asoc.2013.01.003","article-title":"Handling qualitative aspects in unequal area facility layout problem: An interactive genetic algorithm","volume":"13","author":"Pierreval","year":"2013","journal-title":"Appl. Soft Comput."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.ejor.2015.04.029","article-title":"A biased random-key genetic algorithm for the unequal area facility layout problem","volume":"246","author":"Resende","year":"2015","journal-title":"Eur. J. Oper. Res."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.compbiomed.2014.05.002","article-title":"Segmentation of colon tissue sample images using multiple graphics accelerators","volume":"51","year":"2014","journal-title":"Comput. Biol. Med."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Sun, X., Lai, L.F., Chou, P., Chen, L.R., and Wu, C.C. (2018). On GPU implementation of the island model genetic algorithm for solving the unequal area facility layout problem. Appl. Sci., 8.","DOI":"10.3390\/app8091604"},{"key":"ref_33","unstructured":"Melab, N., and Talbi, E.G. (2010, January 7\u201311). GPU-based island model for evolutionary algorithms. Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation, Porland, OR, USA."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"e3797","DOI":"10.1002\/cpe.3797","article-title":"Comparison of common parallel architectures for the execution of the island model and the global parallelization of evolutionary algorithms","volume":"29","author":"Limmer","year":"2016","journal-title":"Concur. Comput. Pract. Exp."},{"key":"ref_35","unstructured":"Tong, X. (1991). SECOT: A sequential Construction Technique for Facility Design. [Ph.D. Thesis, University of Pittsburg]."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1016\/j.cpc.2017.05.019","article-title":"An MPI-CUDA approach for hypersonic flows with detailed state-to-state air kinetics using a GPU cluster","volume":"219","author":"Bonelli","year":"2017","journal-title":"Comput. Phys. Commun."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Han, T.D., and Abdelrahman, T.S. (2011, January 5). Reducing branch divergence in GPU programs. Proceedings of the Fourth Workshop on General Purpose Processing on Graphics Processing Units, Newport Beach, CA, USA.","DOI":"10.1145\/1964179.1964184"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Jachym, M., Lavernhe, S., Euzenat, C., and Tournier, C. (2018). Effective NC machining simulation with OptiX ray tracing engine. Vis. Comput., 1\u20138.","DOI":"10.1007\/s00371-018-1497-7"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1687814017707413","DOI":"10.1177\/1687814017707413","article-title":"Parallel genetic algorithms on the graphics processing units using island model and simulated annealing","volume":"9","author":"Li","year":"2017","journal-title":"Adv. Mech. Eng."},{"key":"ref_40","unstructured":"NVIDIA (2018, August 11). Whitepaper NVIDIA GeForce GTX 980. Available online: http:\/\/international.download.nvidia.com\/geforce-com\/international\/pdfs\/GeForce_GTX_980_Whitepaper_FINAL.PDF."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Whitley, D., Rana, S., and Heckendorn, R. B. (1997). Island model genetic algorithms and linearly separable problems. AISB International Workshop on Evolutionary Computing, Springer.","DOI":"10.1007\/BFb0027170"}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/11\/3\/318\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:35:52Z","timestamp":1760186152000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/11\/3\/318"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,3,2]]},"references-count":41,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2019,3]]}},"alternative-id":["sym11030318"],"URL":"https:\/\/doi.org\/10.3390\/sym11030318","relation":{},"ISSN":["2073-8994"],"issn-type":[{"type":"electronic","value":"2073-8994"}],"subject":[],"published":{"date-parts":[[2019,3,2]]}}}