Efficient parallel and fast convergence chaotic Jaya algorithms
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Título: | Efficient parallel and fast convergence chaotic Jaya algorithms |
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Autor/es: | Migallón Gomis, Héctor | Jimeno-Morenilla, Antonio | 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: | Optimisation | Jaya algorithm | Chaotic map | Parallel algorithms | OpenMP |
Área/s de conocimiento: | Arquitectura y Tecnología de Computadores |
Fecha de publicación: | ago-2020 |
Editor: | Elsevier |
Cita bibliográfica: | Swarm and Evolutionary Computation. 2020, 56: 100698. https://doi.org/10.1016/j.swevo.2020.100698 |
Resumen: | The Jaya algorithm is a recent heuristic approach for solving optimisation problems. It involves a random search for the global optimum, based on the generation of new individuals using both the best and the worst individuals in the population, thus moving solutions towards the optimum while avoiding the worst current solution. In addition to its performance in terms of optimisation, a lack of control parameters is another significant advantage of this algorithm. However, the number of iterations needed to reach the optimal solution, or close to it, may be very high, and the computational cost can hamper compliance with time requirements. In this work, a chaotic two-dimensional (2D) map is used to accelerate convergence, and parallel algorithms are developed to alleviate the computational cost. Coarse- and fine-grained parallel algorithms are developed, the former based on multi-populations and the latter at the individual level, and in both cases these are accelerated by an improved (computational) use of the chaos map. |
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/10045/110905 |
ISSN: | 2210-6502 (Print) | 2210-6510 (Online) |
DOI: | 10.1016/j.swevo.2020.100698 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | © 2020 Elsevier B.V. |
Revisión científica: | si |
Versión del editor: | https://doi.org/10.1016/j.swevo.2020.100698 |
Aparece en las colecciones: | INV - UNICAD - Artículos de Revistas |
Archivos en este ítem:
Archivo | Descripción | Tamaño | Formato | |
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Migallon_etal_2020_SwarmEvolutComput_final.pdf | Versión final (acceso restringido) | 640,31 kB | Adobe PDF | Abrir Solicitar una copia |
Migallon_etal_2020_SwarmEvolutComput_revised.pdf | Versión revisada (acceso abierto) | 1,69 MB | Adobe PDF | Abrir Vista previa |
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