Jaya optimization algorithm with GPU acceleration

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Title: Jaya optimization algorithm with GPU acceleration
Authors: Jimeno-Morenilla, Antonio | Sanchez-Romero, Jose-Luis | Migallón Gomis, Héctor | Mora, Higinio
Research Group/s: UniCAD: Grupo de investigación en CAD/CAM/CAE de la Universidad de Alicante | Informática Industrial y Redes de Computadores
Center, Department or Service: Universidad de Alicante. Departamento de Tecnología Informática y Computación
Keywords: Jaya | Optimization | Parallelism | GPU | CUDA
Knowledge Area: Arquitectura y Tecnología de Computadores
Issue Date: Mar-2019
Publisher: Springer US
Citation: The Journal of Supercomputing. 2019, 75(3): 1094-1106. doi:10.1007/s11227-018-2316-7
Abstract: Optimization methods allow looking for an optimal value given a specific function within a constrained or unconstrained domain. These methods are useful for a wide range of scientific and engineering applications. Recently, a new optimization method called Jaya has generated growing interest because of its simplicity and efficiency. In this paper, we present the Jaya GPU-based parallel algorithms we developed and analyze both parallel performance and optimization performance using a well-known benchmark of unconstrained functions. Results indicate that parallel Jaya implementation achieves significant speed-up for all benchmark functions, obtaining speed-ups of up to 190×, without affecting optimization performance.
Sponsor: This research was supported by the Spanish Ministry of Economy and Competitiveness under Grant TIN2015-66972-C5-4-R, co-financed by FEDER funds (MINECO/FEDER/UE).
URI: http://hdl.handle.net/10045/91221
ISSN: 0920-8542 (Print) | 1573-0484 (Online)
DOI: 10.1007/s11227-018-2316-7
Language: eng
Type: info:eu-repo/semantics/article
Rights: © Springer Science+Business Media, LLC, part of Springer Nature 2018
Peer Review: si
Publisher version: https://doi.org/10.1007/s11227-018-2316-7
Appears in Collections:INV - UNICAD - Artículos de Revistas
INV - I2RC - Artículos de Revistas

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