Please use this identifier to cite or link to this item: https://doi.org/10.1038/s41598-021-90847-7
Title: Three novel quantum-inspired swarm optimization algorithms using different bounded potential fields
Authors: Alvarez-Alvarado, Manuel S.
Alban-Chacón, Francisco E.
Lamilla-Rubio, Erick A.
Rodríguez-Gallegos, Carlos D. 
Velásquez, Washington
Issue Date: 2-Jun-2021
Publisher: Nature Research
Citation: Alvarez-Alvarado, Manuel S., Alban-Chacón, Francisco E., Lamilla-Rubio, Erick A., Rodríguez-Gallegos, Carlos D., Velásquez, Washington (2021-06-02). Three novel quantum-inspired swarm optimization algorithms using different bounded potential fields. Scientific Reports 11 (1) : 11655. ScholarBank@NUS Repository. https://doi.org/10.1038/s41598-021-90847-7
Rights: Attribution 4.0 International
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. © 2021, The Author(s).
Source Title: Scientific Reports
URI: https://scholarbank.nus.edu.sg/handle/10635/233053
ISSN: 2045-2322
DOI: 10.1038/s41598-021-90847-7
Rights: Attribution 4.0 International
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