Parallel Genetic Algorithms' Implementation Using a Scalable Concurrent Operation in Python

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dc.contributor.authorŠkorpil, Vladislavcs
dc.contributor.authorOujezský, Václavcs
dc.coverage.issue6cs
dc.coverage.volume22cs
dc.date.issued2022-03-20cs
dc.description.abstractThis paper presents an implementation of the parallelization of genetic algorithms. Three models of parallelized genetic algorithms are presented, namely the Master-Slave genetic algorithm, the Coarse-Grained genetic algorithm, and the Fine-Grained genetic algorithm. Furthermore, these models are compared with the basic serial genetic algorithm model. Four modules, Multiprocessing, Celery, PyCSP, and Scalable Concurrent Operation in Python, were investigated among the many parallelization options in Python. The Scalable Concurrent Operation in Python was selected as the most favorable option, so the models were implemented using the Python programming language, RabbitMQ, and SCOOP. Based on the implementation results and testing performed, a comparison of the hardware utilization of each deployed model is provided. The results' implementation using SCOOP was investigated from three aspects. The first aspect was the parallelization and integration of the SCOOP module into the resulting Python module. The second was the communication within the genetic algorithm topology. The third aspect was the performance of the parallel genetic algorithm model depending on the hardware.en
dc.formattextcs
dc.format.extent1-19cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationSENSORS. 2022, vol. 22, issue 6, p. 1-19.en
dc.identifier.doi10.3390/s22062389cs
dc.identifier.issn1424-8220cs
dc.identifier.orcid0000-0003-0957-0563cs
dc.identifier.orcid0000-0001-7629-6299cs
dc.identifier.other177629cs
dc.identifier.researcheridQ-9784-2017cs
dc.identifier.scopus57160133400cs
dc.identifier.urihttp://hdl.handle.net/11012/204169
dc.language.isoencs
dc.publisherMDPIcs
dc.relation.ispartofSENSORScs
dc.relation.urihttps://www.mdpi.com/1424-8220/22/6/2389cs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/1424-8220/cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectMaster-Slaveen
dc.subjectCoarse-Graineden
dc.subjectFine-Graineden
dc.subjectparallelized genetic algorithmsen
dc.subjectSCOOPen
dc.titleParallel Genetic Algorithms' Implementation Using a Scalable Concurrent Operation in Pythonen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-177629en
sync.item.dbtypeVAVen
sync.item.insts2025.02.03 15:42:23en
sync.item.modts2025.01.17 18:33:00en
thesis.grantorVysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií. Ústav telekomunikacícs
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