Multi-level parallel chaotic Jaya optimization algorithms for solving constrained engineering design problems
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Título: | Multi-level parallel chaotic Jaya optimization algorithms for solving constrained engineering design problems |
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Autor/es: | Migallón Gomis, Héctor | Jimeno-Morenilla, Antonio | Rico, Héctor | Sanchez-Romero, Jose-Luis | Belazi, Akram |
Grupo/s de investigación o GITE: | UniCAD: Grupo de investigación en CAD/CAM/CAE de la Universidad de Alicante |
Centro, Departamento o Servicio: | Universidad de Alicante. Departamento de Tecnología Informática y Computación |
Palabras clave: | Optimization | Constrained engineering problem | Jaya algorithm | Chaotic map | Parallel algorithms | OpenMP |
Área/s de conocimiento: | Arquitectura y Tecnología de Computadores |
Fecha de publicación: | 6-abr-2021 |
Editor: | Springer Nature |
Cita bibliográfica: | The Journal of Supercomputing. 2021, 77: 12280-12319. https://doi.org/10.1007/s11227-021-03737-0 |
Resumen: | Several heuristic optimization algorithms have been applied to solve engineering problems. Most of these algorithms are based on populations that evolve according to different rules and parameters to reach the optimal value of a function cost through an iterative process. Different parallel strategies have been proposed to accelerate these algorithms. In this work, we combined coarse-grained strategies, based on multi-populations, with fine-grained strategies, based on a diffusion grid, to efficiently use a large number of processes, thus drastically decreasing the computing time. The Chaotic Jaya optimization algorithm has been considered in this work due to its good optimization and computational behaviors in solving both the constrained optimization engineering problems (seven problems) and the unconstrained benchmark functions (a set of 18 functions). The experimental results show that the proposed parallel algorithms outperform the state-of-the-art algorithms in terms of optimization behavior, according to the quality of the obtained solutions, and efficiently exploit shared memory parallel platforms. |
Patrocinador/es: | This research was supported by the Spanish Ministry of Science, Innovation and Universities and the Research State Agency under Grant RTI2018-098156-B-C54 co-financed by FEDER funds, and by the Spanish Ministry of Economy and Competitiveness under Grant TIN2017-89266-R, co-financed by FEDER funds. |
URI: | http://hdl.handle.net/10045/115026 |
ISSN: | 0920-8542 (Print) | 1573-0484 (Online) |
DOI: | 10.1007/s11227-021-03737-0 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
Revisión científica: | si |
Versión del editor: | https://doi.org/10.1007/s11227-021-03737-0 |
Aparece en las colecciones: | INV - UNICAD - Artículos de Revistas |
Archivos en este ítem:
Archivo | Descripción | Tamaño | Formato | |
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Migallon_etal_2021_JSupercomput_final.pdf | Versión final (acceso restringido) | 2,99 MB | Adobe PDF | Abrir Solicitar una copia |
Migallon_etal_2021_JSupercomput_preprint.pdf | Preprint (acceso abierto) | 1,01 MB | Adobe PDF | Abrir Vista previa |
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