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Computational Intelligence and Neuroscience
Volume 2017, Article ID 1583847, 11 pages
https://doi.org/10.1155/2017/1583847
Research Article

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

1Information Engineering School, Nanchang University, Nanchang, Jiangxi Province 330031, China
2Economics Management School, Nanchang University, Nanchang, Jiangxi Province 330031, China

Correspondence should be addressed to Xiaohua Nie; moc.361@hoaixein

Received 14 January 2017; Revised 23 March 2017; Accepted 11 September 2017; Published 17 October 2017

Academic Editor: Athanasios Voulodimos

Copyright © 2017 Xiaohua Nie et al. 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.

Abstract

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.