Three novel quantum-inspired swarm optimization algorithms using different bounded potential fields

Abstract Based on the behavior of the quantum particles, it is possible to formulate mathematical expressions to develop metaheuristic search optimization algorithms. This paper presents three novel quantum-inspired algorithms, which scenario is a particle swarm that is excited by a Lorentz, Rosen–M...

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Autores principales:	Manuel S. Alvarez-Alvarado, Francisco E. Alban-Chacón, Erick A. Lamilla-Rubio, Carlos D. Rodríguez-Gallegos, Washington Velásquez
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/e194147ee12f458d9a24fe6c513a6c84
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spelling	oai:doaj.org-article:e194147ee12f458d9a24fe6c513a6c842021-12-02T18:25:04ZThree novel quantum-inspired swarm optimization algorithms using different bounded potential fields10.1038/s41598-021-90847-72045-2322https://doaj.org/article/e194147ee12f458d9a24fe6c513a6c842021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-90847-7https://doaj.org/toc/2045-2322Abstract Based on the behavior of the quantum particles, it is possible to formulate mathematical expressions to develop metaheuristic search optimization algorithms. This paper presents three novel quantum-inspired algorithms, which scenario is a particle swarm that is excited by a Lorentz, Rosen–Morse, and Coulomb-like square root potential fields, respectively. To show the computational efficacy of the proposed optimization techniques, the paper presents a comparative study with the classical particle swarm optimization (PSO), genetic algorithm (GA), and firefly algorithm (FFA). The algorithms are used to solve 24 benchmark functions that are categorized by unimodal, multimodal, and fixed-dimension multimodal. As a finding, the algorithm inspired in the Lorentz potential field presents the most balanced computational performance in terms of exploitation (accuracy and precision), exploration (convergence speed and acceleration), and simulation time compared to the algorithms previously mentioned. A deeper analysis reveals that a strong potential field inside a well with weak asymptotic behavior leads to better exploitation and exploration attributes for unimodal, multimodal, and fixed-multimodal functions.Manuel S. Alvarez-AlvaradoFrancisco E. Alban-ChacónErick A. Lamilla-RubioCarlos D. Rodríguez-GallegosWashington VelásquezNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-22 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Manuel S. Alvarez-Alvarado Francisco E. Alban-Chacón Erick A. Lamilla-Rubio Carlos D. Rodríguez-Gallegos Washington Velásquez Three novel quantum-inspired swarm optimization algorithms using different bounded potential fields
description	Abstract Based on the behavior of the quantum particles, it is possible to formulate mathematical expressions to develop metaheuristic search optimization algorithms. This paper presents three novel quantum-inspired algorithms, which scenario is a particle swarm that is excited by a Lorentz, Rosen–Morse, and Coulomb-like square root potential fields, respectively. To show the computational efficacy of the proposed optimization techniques, the paper presents a comparative study with the classical particle swarm optimization (PSO), genetic algorithm (GA), and firefly algorithm (FFA). The algorithms are used to solve 24 benchmark functions that are categorized by unimodal, multimodal, and fixed-dimension multimodal. As a finding, the algorithm inspired in the Lorentz potential field presents the most balanced computational performance in terms of exploitation (accuracy and precision), exploration (convergence speed and acceleration), and simulation time compared to the algorithms previously mentioned. A deeper analysis reveals that a strong potential field inside a well with weak asymptotic behavior leads to better exploitation and exploration attributes for unimodal, multimodal, and fixed-multimodal functions.
format	article
author	Manuel S. Alvarez-Alvarado Francisco E. Alban-Chacón Erick A. Lamilla-Rubio Carlos D. Rodríguez-Gallegos Washington Velásquez
author_facet	Manuel S. Alvarez-Alvarado Francisco E. Alban-Chacón Erick A. Lamilla-Rubio Carlos D. Rodríguez-Gallegos Washington Velásquez
author_sort	Manuel S. Alvarez-Alvarado
title	Three novel quantum-inspired swarm optimization algorithms using different bounded potential fields
title_short	Three novel quantum-inspired swarm optimization algorithms using different bounded potential fields
title_full	Three novel quantum-inspired swarm optimization algorithms using different bounded potential fields
title_fullStr	Three novel quantum-inspired swarm optimization algorithms using different bounded potential fields
title_full_unstemmed	Three novel quantum-inspired swarm optimization algorithms using different bounded potential fields
title_sort	three novel quantum-inspired swarm optimization algorithms using different bounded potential fields
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/e194147ee12f458d9a24fe6c513a6c84
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Three novel quantum-inspired swarm optimization algorithms using different bounded potential fields

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