Parallel genetic algorithms with migration for the hybrid flow shop scheduling problem
type: Item
instance of: scholarly article
title: Parallel genetic algorithms with migration for the hybrid flow shop scheduling problem
zbMATH Open document ID: 1172.90513
DOI: 10.1155/JAMDS/2006/65746
author: K. Belkadi
author: M. Benyettou
author: Michel Gourgand
published in: Journal of Applied Mathematics and Decision Sciences
publication date: 2008-11-20T00:00:00Z
full work available at URL: https://eudml.org/doc/130729
review text: Summary: This paper addresses scheduling problems in hybrid flow shop-like systems with a migration parallel genetic algorithm (PGA{\_}MIG). This parallel genetic algorithm model allows genetic diversity by the application of selection and reproduction mechanisms nearer to nature. The space structure of the population is modified by dividing it into disjoined subpopulations. From time to time, individuals are exchanged between the different subpopulations (migration). Influence of parameters and dedicated strategies are studied. These parameters are the number of independent subpopulations, the interconnection topology between subpopulations, the choice/replacement strategy of the migrant individuals, and the migration frequency. A comparison between the sequential and parallel version of genetic algorithm (GA) is provided. This comparison relates to the quality of the solution and the execution time of the two versions. The efficiency of the parallel model highly depends on the parameters and especially on the migration frequency. In the same way this parallel model gives a significant improvement of computational time if it is implemented on a parallel architecture which offers an acceptable number of processors (as many processors as subpopulations).
Mathematics Subject Classification ID: 90C59
Mathematics Subject Classification ID: 90B35
Mathematics Subject Classification ID: 62L99
zbMATH DE Number: 5369118
MaRDI profile type: Publication
cites work: Les problèmes d'ordonnancement de type flow-shop hybride : état de l'art
cites work: Q4692508
cites work: Q4079017
cites work: Learning systems in intelligent control: an appraisal of fuzzy, neural and genetic algorithm control applications
OpenAlex ID: W2144649562
description: scientific article; zbMATH DE number 5369118