Cargando…

An improved adaptive memetic differential evolution optimization algorithms for data clustering problems

The performance of data clustering algorithms is mainly dependent on their ability to balance between the exploration and exploitation of the search process. Although some data clustering algorithms have achieved reasonable quality solutions for some datasets, their performance across real-life data...

Descripción completa

Detalles Bibliográficos
Autores principales: Mustafa, Hossam M. J., Ayob, Masri, Nazri, Mohd Zakree Ahmad, Kendall, Graham
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6538400/
https://www.ncbi.nlm.nih.gov/pubmed/31137034
http://dx.doi.org/10.1371/journal.pone.0216906
_version_ 1783422175427952640
author Mustafa, Hossam M. J.
Ayob, Masri
Nazri, Mohd Zakree Ahmad
Kendall, Graham
author_facet Mustafa, Hossam M. J.
Ayob, Masri
Nazri, Mohd Zakree Ahmad
Kendall, Graham
author_sort Mustafa, Hossam M. J.
collection PubMed
description The performance of data clustering algorithms is mainly dependent on their ability to balance between the exploration and exploitation of the search process. Although some data clustering algorithms have achieved reasonable quality solutions for some datasets, their performance across real-life datasets could be improved. This paper proposes an adaptive memetic differential evolution optimisation algorithm (AMADE) for addressing data clustering problems. The memetic algorithm (MA) employs an adaptive differential evolution (DE) mutation strategy, which can offer superior mutation performance across many combinatorial and continuous problem domains. By hybridising an adaptive DE mutation operator with the MA, we propose that it can lead to faster convergence and better balance the exploration and exploitation of the search. We would also expect that the performance of AMADE to be better than MA and DE if executed separately. Our experimental results, based on several real-life benchmark datasets, shows that AMADE outperformed other compared clustering algorithms when compared using statistical analysis. We conclude that the hybridisation of MA and the adaptive DE is a suitable approach for addressing data clustering problems and can improve the balance between global exploration and local exploitation of the optimisation algorithm.
format Online
Article
Text
id pubmed-6538400
institution National Center for Biotechnology Information
language English
publishDate 2019
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-65384002019-06-05 An improved adaptive memetic differential evolution optimization algorithms for data clustering problems Mustafa, Hossam M. J. Ayob, Masri Nazri, Mohd Zakree Ahmad Kendall, Graham PLoS One Research Article The performance of data clustering algorithms is mainly dependent on their ability to balance between the exploration and exploitation of the search process. Although some data clustering algorithms have achieved reasonable quality solutions for some datasets, their performance across real-life datasets could be improved. This paper proposes an adaptive memetic differential evolution optimisation algorithm (AMADE) for addressing data clustering problems. The memetic algorithm (MA) employs an adaptive differential evolution (DE) mutation strategy, which can offer superior mutation performance across many combinatorial and continuous problem domains. By hybridising an adaptive DE mutation operator with the MA, we propose that it can lead to faster convergence and better balance the exploration and exploitation of the search. We would also expect that the performance of AMADE to be better than MA and DE if executed separately. Our experimental results, based on several real-life benchmark datasets, shows that AMADE outperformed other compared clustering algorithms when compared using statistical analysis. We conclude that the hybridisation of MA and the adaptive DE is a suitable approach for addressing data clustering problems and can improve the balance between global exploration and local exploitation of the optimisation algorithm. Public Library of Science 2019-05-28 /pmc/articles/PMC6538400/ /pubmed/31137034 http://dx.doi.org/10.1371/journal.pone.0216906 Text en © 2019 Mustafa et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Mustafa, Hossam M. J.
Ayob, Masri
Nazri, Mohd Zakree Ahmad
Kendall, Graham
An improved adaptive memetic differential evolution optimization algorithms for data clustering problems
title An improved adaptive memetic differential evolution optimization algorithms for data clustering problems
title_full An improved adaptive memetic differential evolution optimization algorithms for data clustering problems
title_fullStr An improved adaptive memetic differential evolution optimization algorithms for data clustering problems
title_full_unstemmed An improved adaptive memetic differential evolution optimization algorithms for data clustering problems
title_short An improved adaptive memetic differential evolution optimization algorithms for data clustering problems
title_sort improved adaptive memetic differential evolution optimization algorithms for data clustering problems
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6538400/
https://www.ncbi.nlm.nih.gov/pubmed/31137034
http://dx.doi.org/10.1371/journal.pone.0216906
work_keys_str_mv AT mustafahossammj animprovedadaptivememeticdifferentialevolutionoptimizationalgorithmsfordataclusteringproblems
AT ayobmasri animprovedadaptivememeticdifferentialevolutionoptimizationalgorithmsfordataclusteringproblems
AT nazrimohdzakreeahmad animprovedadaptivememeticdifferentialevolutionoptimizationalgorithmsfordataclusteringproblems
AT kendallgraham animprovedadaptivememeticdifferentialevolutionoptimizationalgorithmsfordataclusteringproblems
AT mustafahossammj improvedadaptivememeticdifferentialevolutionoptimizationalgorithmsfordataclusteringproblems
AT ayobmasri improvedadaptivememeticdifferentialevolutionoptimizationalgorithmsfordataclusteringproblems
AT nazrimohdzakreeahmad improvedadaptivememeticdifferentialevolutionoptimizationalgorithmsfordataclusteringproblems
AT kendallgraham improvedadaptivememeticdifferentialevolutionoptimizationalgorithmsfordataclusteringproblems