Comparison of High Performance Parallel Implementations of TLBO and Jaya Optimization Methods on Manycore GPU
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Title: | Comparison of High Performance Parallel Implementations of TLBO and Jaya Optimization Methods on Manycore GPU |
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Authors: | Rico, Héctor | Sanchez-Romero, Jose-Luis | Jimeno-Morenilla, Antonio | Migallón Gomis, Héctor | Mora, Higinio | Rao, Ravipudi Venkata |
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: | CUDA | GPU | Jaya | TLBO | Optimization | Parallelism |
Knowledge Area: | Arquitectura y Tecnología de Computadores |
Issue Date: | 12-Sep-2019 |
Publisher: | IEEE |
Citation: | IEEE Access. 2019, 7: 133822-133831. doi:10.1109/ACCESS.2019.2941086 |
Abstract: | The utilization of optimization algorithms within engineering problems has had a major rise in recent years, which has led to the proliferation of a large number of new algorithms to solve optimization problems. In addition, the emergence of new parallelization techniques applicable to these algorithms to improve their convergence time has made it a subject of study by many authors. Recently, two optimization algorithms have been developed: Teaching-Learning Based Optimization and Jaya. One of the main advantages of both algorithms over other optimization methods is that the former do not need to adjust specific parameters for the particular problem to which they are applied. In this paper, the parallel implementations of Teaching-Learning Based Optimization and Jaya are compared. The parallelization of both algorithms is performed using manycore GPU techniques. Different scenarios will be created involving functions frequently applied to the evaluation of optimization algorithms. Results will make it possible to compare both parallel algorithms with regard to the number of iterations and the time needed to perform them so as to obtain a predefined error level. The GPU resources occupation in each case will also be analyzed. |
Sponsor: | This work was supported in part by the Spanish Ministry of Economy and Competitiveness under Grant TIN2017-89266-R, in part by FEDER funds (MINECO/FEDER/UE), and in part by the Spanish Ministry of Science, Innovation, and Universities co-financed by FEDER funds under Grant RTI2018-098156-B-C54. |
URI: | http://hdl.handle.net/10045/96949 |
ISSN: | 2169-3536 |
DOI: | 10.1109/ACCESS.2019.2941086 |
Language: | eng |
Type: | info:eu-repo/semantics/article |
Rights: | © 2019 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. |
Peer Review: | si |
Publisher version: | https://doi.org/10.1109/ACCESS.2019.2941086 |
Appears in Collections: | INV - AIA - Artículos de Revistas INV - I2RC - Artículos de Revistas INV - UNICAD - Artículos de Revistas |
Files in This Item:
File | Description | Size | Format | |
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2019_Rico-Garcia_etal_IEEEAccess.pdf | 8,12 MB | Adobe PDF | Open Preview | |
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