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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2019
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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 |
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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 |
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