Applying Parallel and Distributed Models on Bio-Inspired Algorithms via a Clustering Method
In the world of optimization, especially concerning metaheuristics, solving complex problems represented by applying big data and constraint instances can be difficult. This is mainly due to the difficulty of implementing efficient solutions that can solve complex optimization problems in adequate t...
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| Format: | Article |
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MDPI AG
2022-01-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/10/2/274 |
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| author | Álvaro Gómez-Rubio Ricardo Soto Broderick Crawford Adrián Jaramillo David Mancilla Carlos Castro Rodrigo Olivares |
| author_facet | Álvaro Gómez-Rubio Ricardo Soto Broderick Crawford Adrián Jaramillo David Mancilla Carlos Castro Rodrigo Olivares |
| author_sort | Álvaro Gómez-Rubio |
| collection | DOAJ |
| description | In the world of optimization, especially concerning metaheuristics, solving complex problems represented by applying big data and constraint instances can be difficult. This is mainly due to the difficulty of implementing efficient solutions that can solve complex optimization problems in adequate time, which do exist in different industries. Big data has demonstrated its efficiency in solving different concerns in information management. In this paper, an approach based on multiprocessing is proposed wherein clusterization and parallelism are used together to improve the search process of metaheuristics when solving large instances of complex optimization problems, incorporating collaborative elements that enhance the quality of the solution. The proposal deals with machine learning algorithms to improve the segmentation of the search space. Particularly, two different clustering methods belonging to automatic learning techniques, are implemented on bio-inspired algorithms to smartly initialize their solution population, and then organize the resolution from the beginning of the search. The results show that this approach is competitive with other techniques in solving a large set of cases of a well-known NP-hard problem without incorporating too much additional complexity into the metaheuristic algorithms. |
| first_indexed | 2024-03-10T01:01:01Z |
| format | Article |
| id | doaj.art-d4046981cddc41d8972a9e428fd73bf5 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T01:01:01Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-d4046981cddc41d8972a9e428fd73bf52023-11-23T14:35:02ZengMDPI AGMathematics2227-73902022-01-0110227410.3390/math10020274Applying Parallel and Distributed Models on Bio-Inspired Algorithms via a Clustering MethodÁlvaro Gómez-Rubio0Ricardo Soto1Broderick Crawford2Adrián Jaramillo3David Mancilla4Carlos Castro5Rodrigo Olivares6Escuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Valparaíso 2362807, ChileEscuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Valparaíso 2362807, ChileEscuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Valparaíso 2362807, ChileEscuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Valparaíso 2362807, ChileIndependent Researcher, Valparaíso 2362807, ChileDepartamento de Informática, Universidad Técnica Federico Santa María, Valparaíso 2390123, ChileEscuela de Ingeniería Informática, Universidad de Valparaíso, Valparaíso 2362905, ChileIn the world of optimization, especially concerning metaheuristics, solving complex problems represented by applying big data and constraint instances can be difficult. This is mainly due to the difficulty of implementing efficient solutions that can solve complex optimization problems in adequate time, which do exist in different industries. Big data has demonstrated its efficiency in solving different concerns in information management. In this paper, an approach based on multiprocessing is proposed wherein clusterization and parallelism are used together to improve the search process of metaheuristics when solving large instances of complex optimization problems, incorporating collaborative elements that enhance the quality of the solution. The proposal deals with machine learning algorithms to improve the segmentation of the search space. Particularly, two different clustering methods belonging to automatic learning techniques, are implemented on bio-inspired algorithms to smartly initialize their solution population, and then organize the resolution from the beginning of the search. The results show that this approach is competitive with other techniques in solving a large set of cases of a well-known NP-hard problem without incorporating too much additional complexity into the metaheuristic algorithms.https://www.mdpi.com/2227-7390/10/2/274distributed metaheuristicsparallel metaheuristicbig data clusteringoptimization problems |
| spellingShingle | Álvaro Gómez-Rubio Ricardo Soto Broderick Crawford Adrián Jaramillo David Mancilla Carlos Castro Rodrigo Olivares Applying Parallel and Distributed Models on Bio-Inspired Algorithms via a Clustering Method Mathematics distributed metaheuristics parallel metaheuristic big data clustering optimization problems |
| title | Applying Parallel and Distributed Models on Bio-Inspired Algorithms via a Clustering Method |
| title_full | Applying Parallel and Distributed Models on Bio-Inspired Algorithms via a Clustering Method |
| title_fullStr | Applying Parallel and Distributed Models on Bio-Inspired Algorithms via a Clustering Method |
| title_full_unstemmed | Applying Parallel and Distributed Models on Bio-Inspired Algorithms via a Clustering Method |
| title_short | Applying Parallel and Distributed Models on Bio-Inspired Algorithms via a Clustering Method |
| title_sort | applying parallel and distributed models on bio inspired algorithms via a clustering method |
| topic | distributed metaheuristics parallel metaheuristic big data clustering optimization problems |
| url | https://www.mdpi.com/2227-7390/10/2/274 |
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