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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F22%3APU144347" target="_blank" >RIV/00216305:26220/22:PU144347 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.mdpi.com/1424-8220/22/6/2389" target="_blank" >https://www.mdpi.com/1424-8220/22/6/2389</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/s22062389" target="_blank" >10.3390/s22062389</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

<a href="/cs/project/VI20192022135" target="_blank" >VI20192022135: Hloubková hardwarová detekce síťového provozu pasivních optických sítí nové generace v kritických infrastrukturách</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

SENSORS

ISSN

1424-8220

e-ISSN

1424-3210

Svazek periodika

22

Číslo periodika v rámci svazku

6

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

19

Strana od-do

1-19

Kód UT WoS článku

000774393200001

EID výsledku v databázi Scopus

2-s2.0-85126899544