An Improved ABC Algorithm and Its Application on Parameter Identification of Solar Cell Model

In order to enhance the searching ability, an improved artificial bee colony algorithm (IABC) is proposed. In the employed bee stage, the global optimal honey source is introduced to guide the neighborhood search, the reversed learning mechanism is also introduced to increase the diversity of the population and the MR parameters is used to control the numbers of updating dimension. In the onlooker bee stage, quantum behavior is introduced to improve the convergent speed and accuracy of the algorithm. IABC is applied to solar cell model parameter identification. The experimental results show that the performance of parameter identification is improved by IABC. IABC provides a new method for solar cell parameter identification.