Parallel Improvements of the Jaya Optimization Algorithm
Por favor, use este identificador para citar o enlazar este ítem:
http://hdl.handle.net/10045/75695
Título: | Parallel Improvements of the Jaya Optimization Algorithm |
---|---|
Autor/es: | Migallón Gomis, Héctor | Jimeno-Morenilla, Antonio | Sanchez-Romero, Jose-Luis |
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: | Jaya | Optimization problems | Parallel | Heuristic | OpenMP | MPI | Hybrid MPI/OpenMP |
Área/s de conocimiento: | Arquitectura y Tecnología de Computadores |
Fecha de publicación: | 18-may-2018 |
Editor: | MDPI |
Cita bibliográfica: | Migallón H, Jimeno-Morenilla A, Sanchez-Romero J-L. Parallel Improvements of the Jaya Optimization Algorithm. Applied Sciences. 2018; 8(5):819. doi:10.3390/app8050819 |
Resumen: | A wide range of applications use optimization algorithms to find an optimal value, often a minimum one, for a given function. Depending on the application, both the optimization algorithm’s behavior, and its computational time, can prove to be critical issues. In this paper, we present our efficient parallel proposals of the Jaya algorithm, a recent optimization algorithm that enables one to solve constrained and unconstrained optimization problems. We tested parallel Jaya algorithms for shared, distributed, and heterogeneous memory platforms, obtaining good parallel performance while leaving Jaya algorithm behavior unchanged. Parallel performance was analyzed using 30 unconstrained functions reaching a speed-up of up to 57.6x using 60 processors. For all tested functions, the parallel distributed memory algorithm obtained parallel efficiencies that were nearly ideal, and combining it with the shared memory algorithm allowed us to obtain good parallel performance. The experimental results show a good parallel performance regardless of the nature of the function to be optimized. |
Patrocinador/es: | This research was supported by the Spanish Ministry of Economy and Competitiveness under Grants TIN2015-66972-C5-4-R and TIN2017-89266-R, co-financed by FEDER funds (MINECO/FEDER/UE). |
URI: | http://hdl.handle.net/10045/75695 |
ISSN: | 2076-3417 |
DOI: | 10.3390/app8050819 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
Revisión científica: | si |
Versión del editor: | https://doi.org/10.3390/app8050819 |
Aparece en las colecciones: | INV - UNICAD - Artículos de Revistas |
Archivos en este ítem:
Archivo | Descripción | Tamaño | Formato | |
---|---|---|---|---|
2018_Migallon_etal_ApplSci.pdf | 823,94 kB | Adobe PDF | Abrir Vista previa | |
Este ítem está licenciado bajo Licencia Creative Commons