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Chaos Quantum-Behaved Cat Swarm Optimization Algorithm and Its Application in the PV MPPT

Cat Swarm Optimization (CSO) algorithm was put forward in 2006. Despite a faster convergence speed compared with Particle Swarm Optimization (PSO) algorithm, the application of CSO is greatly limited by the drawback of “premature convergence,” that is, the possibility of trapping in local optimum wh...

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Autores principales: Nie, Xiaohua, Wang, Wei, Nie, Haoyao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5664373/
https://www.ncbi.nlm.nih.gov/pubmed/29181020
http://dx.doi.org/10.1155/2017/1583847
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author Nie, Xiaohua
Wang, Wei
Nie, Haoyao
author_facet Nie, Xiaohua
Wang, Wei
Nie, Haoyao
author_sort Nie, Xiaohua
collection PubMed
description Cat Swarm Optimization (CSO) algorithm was put forward in 2006. Despite a faster convergence speed compared with Particle Swarm Optimization (PSO) algorithm, the application of CSO is greatly limited by the drawback of “premature convergence,” that is, the possibility of trapping in local optimum when dealing with nonlinear optimization problem with a large number of local extreme values. In order to surmount the shortcomings of CSO, Chaos Quantum-behaved Cat Swarm Optimization (CQCSO) algorithm is proposed in this paper. Firstly, Quantum-behaved Cat Swarm Optimization (QCSO) algorithm improves the accuracy of the CSO algorithm, because it is easy to fall into the local optimum in the later stage. Chaos Quantum-behaved Cat Swarm Optimization (CQCSO) algorithm is proposed by introducing tent map for jumping out of local optimum in this paper. Secondly, CQCSO has been applied in the simulation of five different test functions, showing higher accuracy and less time consumption than CSO and QCSO. Finally, photovoltaic MPPT model and experimental platform are established and global maximum power point tracking control strategy is achieved by CQCSO algorithm, the effectiveness and efficiency of which have been verified by both simulation and experiment.
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spelling pubmed-56643732017-11-27 Chaos Quantum-Behaved Cat Swarm Optimization Algorithm and Its Application in the PV MPPT Nie, Xiaohua Wang, Wei Nie, Haoyao Comput Intell Neurosci Research Article Cat Swarm Optimization (CSO) algorithm was put forward in 2006. Despite a faster convergence speed compared with Particle Swarm Optimization (PSO) algorithm, the application of CSO is greatly limited by the drawback of “premature convergence,” that is, the possibility of trapping in local optimum when dealing with nonlinear optimization problem with a large number of local extreme values. In order to surmount the shortcomings of CSO, Chaos Quantum-behaved Cat Swarm Optimization (CQCSO) algorithm is proposed in this paper. Firstly, Quantum-behaved Cat Swarm Optimization (QCSO) algorithm improves the accuracy of the CSO algorithm, because it is easy to fall into the local optimum in the later stage. Chaos Quantum-behaved Cat Swarm Optimization (CQCSO) algorithm is proposed by introducing tent map for jumping out of local optimum in this paper. Secondly, CQCSO has been applied in the simulation of five different test functions, showing higher accuracy and less time consumption than CSO and QCSO. Finally, photovoltaic MPPT model and experimental platform are established and global maximum power point tracking control strategy is achieved by CQCSO algorithm, the effectiveness and efficiency of which have been verified by both simulation and experiment. Hindawi 2017 2017-10-17 /pmc/articles/PMC5664373/ /pubmed/29181020 http://dx.doi.org/10.1155/2017/1583847 Text en Copyright © 2017 Xiaohua Nie et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Nie, Xiaohua
Wang, Wei
Nie, Haoyao
Chaos Quantum-Behaved Cat Swarm Optimization Algorithm and Its Application in the PV MPPT
title Chaos Quantum-Behaved Cat Swarm Optimization Algorithm and Its Application in the PV MPPT
title_full Chaos Quantum-Behaved Cat Swarm Optimization Algorithm and Its Application in the PV MPPT
title_fullStr Chaos Quantum-Behaved Cat Swarm Optimization Algorithm and Its Application in the PV MPPT
title_full_unstemmed Chaos Quantum-Behaved Cat Swarm Optimization Algorithm and Its Application in the PV MPPT
title_short Chaos Quantum-Behaved Cat Swarm Optimization Algorithm and Its Application in the PV MPPT
title_sort chaos quantum-behaved cat swarm optimization algorithm and its application in the pv mppt
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5664373/
https://www.ncbi.nlm.nih.gov/pubmed/29181020
http://dx.doi.org/10.1155/2017/1583847
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