摘要
针对萤火虫(FA)算法易陷入局部最优等缺点,提出一种基于混沌优化和高斯变异的萤火虫(CGFA)算法。首先在萤火虫初始位置进行混沌优化,使其分布均匀;然后在萤火虫移动过程中采用高斯变异,使其避免陷入局部最优。同时,将CCGFA算法和FA算法分别应用于三种基准函数,将其进行测试与比较,得出CGFA算法性能优于FA算法的性能。再者,将CGFA算法、FA算法、ABC算法分别应用于光伏MPPT中,通过仿真与实验,验证了CGFA算法的优越性。
To solve the problem that the firefly algorithm is vulnerable to local optimal, the firefly algorithm based on chaos optimization and gaussian variation is proposed. Firstly, the location of the fireflies was initialized by chaotic optimization, and its distribution was uniform. Then, gauss variation is used in the movement of the firefly to avoid getting into local optimum. At the same time, the CGFA algorithm and FA algorithm are applied to three basic functions respectively, and the performance of CGFA algorithm is better than that of FA algorithm by testing and comparison. Moreover, The CGFA algorithm, FA algorithm and ABC algorithm are applied to photovoltaic MPPT respectively. The advantages of CGFA algorithm are verified by simulation and experiment.
作者
武新雯
李虹
刘立群
张聪明
WU Xin-wen;LI Hong;LIU Li-qun;ZHANG Cong-ming(College of Electronic and Information Engineering,Taiyuan University of Science and Technology,Taiyuan 030024,China)
出处
《太原科技大学学报》
2019年第3期201-207,共7页
Journal of Taiyuan University of Science and Technology
基金
山西省应用基础研究项目(201601D011058)
煤矿电气设备与智能控制山西省重点实验室开放课题(MEI201603)
关键词
萤火虫算法
混沌优化
高斯变异
函数优化
光伏MPPT
firefly algorithm
chaotic optimization
gauss mutation
function optimization
photovoltaic MPPT
