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A Db-Scan Binarization Algorithm Applied to Matrix Covering Problems
The integration of machine learning techniques and metaheuristic algorithms is an area of interest due to the great potential for applications. In particular, using these hybrid techniques to solve combinatorial optimization problems (COPs) to improve the quality of the solutions and convergence tim...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6766111/ https://www.ncbi.nlm.nih.gov/pubmed/31636660 http://dx.doi.org/10.1155/2019/3238574 |
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author | García, José Moraga, Paola Valenzuela, Matias Crawford, Broderick Soto, Ricardo Pinto, Hernan Peña, Alvaro Altimiras, Francisco Astorga, Gino |
author_facet | García, José Moraga, Paola Valenzuela, Matias Crawford, Broderick Soto, Ricardo Pinto, Hernan Peña, Alvaro Altimiras, Francisco Astorga, Gino |
author_sort | García, José |
collection | PubMed |
description | The integration of machine learning techniques and metaheuristic algorithms is an area of interest due to the great potential for applications. In particular, using these hybrid techniques to solve combinatorial optimization problems (COPs) to improve the quality of the solutions and convergence times is of great interest in operations research. In this article, the db-scan unsupervised learning technique is explored with the goal of using it in the binarization process of continuous swarm intelligence metaheuristic algorithms. The contribution of the db-scan operator to the binarization process is analyzed systematically through the design of random operators. Additionally, the behavior of this algorithm is studied and compared with other binarization methods based on clusters and transfer functions (TFs). To verify the results, the well-known set covering problem is addressed, and a real-world problem is solved. The results show that the integration of the db-scan technique produces consistently better results in terms of computation time and quality of the solutions when compared with TFs and random operators. Furthermore, when it is compared with other clustering techniques, we see that it achieves significantly improved convergence times. |
format | Online Article Text |
id | pubmed-6766111 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-67661112019-10-21 A Db-Scan Binarization Algorithm Applied to Matrix Covering Problems García, José Moraga, Paola Valenzuela, Matias Crawford, Broderick Soto, Ricardo Pinto, Hernan Peña, Alvaro Altimiras, Francisco Astorga, Gino Comput Intell Neurosci Research Article The integration of machine learning techniques and metaheuristic algorithms is an area of interest due to the great potential for applications. In particular, using these hybrid techniques to solve combinatorial optimization problems (COPs) to improve the quality of the solutions and convergence times is of great interest in operations research. In this article, the db-scan unsupervised learning technique is explored with the goal of using it in the binarization process of continuous swarm intelligence metaheuristic algorithms. The contribution of the db-scan operator to the binarization process is analyzed systematically through the design of random operators. Additionally, the behavior of this algorithm is studied and compared with other binarization methods based on clusters and transfer functions (TFs). To verify the results, the well-known set covering problem is addressed, and a real-world problem is solved. The results show that the integration of the db-scan technique produces consistently better results in terms of computation time and quality of the solutions when compared with TFs and random operators. Furthermore, when it is compared with other clustering techniques, we see that it achieves significantly improved convergence times. Hindawi 2019-09-16 /pmc/articles/PMC6766111/ /pubmed/31636660 http://dx.doi.org/10.1155/2019/3238574 Text en Copyright © 2019 José García et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article García, José Moraga, Paola Valenzuela, Matias Crawford, Broderick Soto, Ricardo Pinto, Hernan Peña, Alvaro Altimiras, Francisco Astorga, Gino A Db-Scan Binarization Algorithm Applied to Matrix Covering Problems |
title | A Db-Scan Binarization Algorithm Applied to Matrix Covering Problems |
title_full | A Db-Scan Binarization Algorithm Applied to Matrix Covering Problems |
title_fullStr | A Db-Scan Binarization Algorithm Applied to Matrix Covering Problems |
title_full_unstemmed | A Db-Scan Binarization Algorithm Applied to Matrix Covering Problems |
title_short | A Db-Scan Binarization Algorithm Applied to Matrix Covering Problems |
title_sort | db-scan binarization algorithm applied to matrix covering problems |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6766111/ https://www.ncbi.nlm.nih.gov/pubmed/31636660 http://dx.doi.org/10.1155/2019/3238574 |
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