Settings-Free Hybrid Metaheuristic General Optimization Methods
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Título: | Settings-Free Hybrid Metaheuristic General Optimization Methods |
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Autor/es: | Migallón Gomis, Héctor | Belazi, Akram | Sanchez-Romero, Jose-Luis | Rico, Héctor | Jimeno-Morenilla, Antonio |
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: | Hybrid optimization algorithms | SCA algorithm | Jaya | 2D chaotic map | TLBO | Rao’s algorithms |
Área/s de conocimiento: | Arquitectura y Tecnología de Computadores |
Fecha de publicación: | 3-jul-2020 |
Editor: | MDPI |
Cita bibliográfica: | Migallón H, Belazi A, Sánchez-Romero J-L, Rico H, Jimeno-Morenilla A. Settings-Free Hybrid Metaheuristic General Optimization Methods. Mathematics. 2020; 8(7):1092. doi:10.3390/math8071092 |
Resumen: | Several population-based metaheuristic optimization algorithms have been proposed in the last decades, none of which are able either to outperform all existing algorithms or to solve all optimization problems according to the No Free Lunch (NFL) theorem. Many of these algorithms behave effectively, under a correct setting of the control parameter(s), when solving different engineering problems. The optimization behavior of these algorithms is boosted by applying various strategies, which include the hybridization technique and the use of chaotic maps instead of the pseudo-random number generators (PRNGs). The hybrid algorithms are suitable for a large number of engineering applications in which they behave more effectively than the thoroughbred optimization algorithms. However, they increase the difficulty of correctly setting control parameters, and sometimes they are designed to solve particular problems. This paper presents three hybridizations dubbed HYBPOP, HYBSUBPOP, and HYBIND of up to seven algorithms free of control parameters. Each hybrid proposal uses a different strategy to switch the algorithm charged with generating each new individual. These algorithms are Jaya, sine cosine algorithm (SCA), Rao’s algorithms, teaching-learning-based optimization (TLBO), and chaotic Jaya. The experimental results show that the proposed algorithms perform better than the original algorithms, which implies the optimal use of these algorithms according to the problem to be solved. One more advantage of the hybrid algorithms is that no prior process of control parameter tuning is needed. |
Patrocinador/es: | This research and APC was funded 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/10045/107953 |
ISSN: | 2227-7390 |
DOI: | 10.3390/math8071092 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | © 2020 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/math8071092 |
Aparece en las colecciones: | INV - UNICAD - Artículos de Revistas |
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