Chaos Quantum-Behaved Cat Swarm Optimization Algorithm and Its Application in the PV MPPT.
Chaos Quantum-Behaved Cat Swarm Optimization Algorithm and Its Application in the PV MPPT.
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DOI：
10.1155/2017/1583847
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发表时间：
2017
影响因子：
--
通讯作者：
Nie H
中科院分区：
文献类型：
--
作者：
Nie X;Wang W;Nie H
猫群优化算法(CSO)于2006年提出。尽管与粒子群优化(PSO)算法相比，CSO算法具有更快的收敛速度，但CSO算法存在“早熟收敛”的缺点，即在处理含有大量局部极值的非线性优化问题时，有可能陷入局部最优，这大大限制了CSO算法的应用。为了克服混沌优化算法的不足，提出了混沌量子行为猫群优化算法。首先，量子行为猫群优化算法(QCSO)提高了CSO算法的精度，因为它在后期容易陷入局部最优。通过引入跳出局部最优的帐篷映射，提出了混沌量子行为猫群优化算法。其次，将CQCSO算法应用于五种不同测试函数的仿真，结果表明CQCSO算法比CSO算法和QCSO算法具有更高的精度和更少的时间消耗。最后，建立了光伏最大功率点跟踪模型和实验平台，利用CQCSO算法实现了全局最大功率点跟踪控制策略，并通过仿真和实验验证了该控制策略的有效性和高效性。
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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影响因子：
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作者：
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