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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2021-06-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-021-90847-7 |
| _version_ | 1818434068624179200 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-14T16:31:07Z |
| format | Article |
| id | doaj.art-e194147ee12f458d9a24fe6c513a6c84 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-12-14T16:31:07Z |
| publishDate | 2021-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-e194147ee12f458d9a24fe6c513a6c842022-12-21T22:54:36ZengNature PortfolioScientific Reports2045-23222021-06-0111112210.1038/s41598-021-90847-7Three novel quantum-inspired swarm optimization algorithms using different bounded potential fieldsManuel S. Alvarez-Alvarado0Francisco E. Alban-Chacón1Erick A. Lamilla-Rubio2Carlos D. Rodríguez-Gallegos3Washington Velásquez4Faculty of Electrical and Computer Engineering, Escuela Superior Politécnica del LitoralFaculty of Natural Science and Mathematics, Escuela Superior Politécnica del LitoralFaculty of Natural Science and Mathematics, Escuela Superior Politécnica del LitoralSolar Energy Research Institute of Singapore (SERIS), National University of Singapore (NUS)Faculty of Electrical and Computer Engineering, Escuela Superior Politécnica del LitoralAbstract 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.https://doi.org/10.1038/s41598-021-90847-7 |
| spellingShingle | 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 Scientific Reports |
| title | 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_short | 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 |
| url | https://doi.org/10.1038/s41598-021-90847-7 |
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