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

Skorpil, Vladislav ; Oujezsky, Vaclav

Sensors (Basel, Switzerland), 2022-03, Vol.22 (6), p.2389 [Periódico revisado por pares]

Switzerland: MDPI AG

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  • Título:
    Parallel Genetic Algorithms' Implementation Using a Scalable Concurrent Operation in Python
  • Autor: Skorpil, Vladislav ; Oujezsky, Vaclav
  • Assuntos: Algorithms ; Celery ; Coarse-Grained ; Communication ; Computers ; Efficiency ; Fine-Grained ; Genetic algorithms ; Master–Slave ; Multiprocessing ; Mutation ; Neighborhoods ; Parallel processing ; parallelized genetic algorithms ; Population ; SCOOP ; Topology
  • É parte de: Sensors (Basel, Switzerland), 2022-03, Vol.22 (6), p.2389
  • Notas: ObjectType-Article-1
    SourceType-Scholarly Journals-1
    ObjectType-Feature-2
    content type line 23
    These authors contributed equally to this work.
    This paper is an extended version of Testing of Python Models of Parallelized Genetic Algorithms. Presented at 2020 43rd International Conference on Telecommunications and Signal Processing (TSP), Milan, Italy, 7–9 July 2020.
  • Descrição: This 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.
  • Editor: Switzerland: MDPI AG
  • Idioma: Inglês
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