Cargando…
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...
Autores principales: | , , |
---|---|
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 |
_version_ | 1783274986217144320 |
---|---|
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. |
format | Online Article Text |
id | pubmed-5664373 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT niexiaohua chaosquantumbehavedcatswarmoptimizationalgorithmanditsapplicationinthepvmppt AT wangwei chaosquantumbehavedcatswarmoptimizationalgorithmanditsapplicationinthepvmppt AT niehaoyao chaosquantumbehavedcatswarmoptimizationalgorithmanditsapplicationinthepvmppt |