Due to the multimodal and nonlinear nature of photovoltaic（PV）models

parameter identification is a challenging problem. In view of the limitations faced by traditional algorithms in the field of PV model parameter identification

such as insufficient reliability

low accuracy

easy to fall into local optimal solutions and premature convergence

a improved complex valued encoding symbiotic organisms search（ICSOS）is proposed for PV model parameter identification. In order to enhance the optimization ability of the traditional symbiotic organism search algorithm

a complex valued encoding is introduced

which expands the original one-dimensional real number coding to a two-dimensional complex coding space

in order to expand the search range of the population and enhance the optimization ability and speed of the algorithm. Simulation validation shows that the proposed improved algorithm has good applicability in the process of parameter identification in single diode model

and PV module model

and compared with other optimization algorithms

the ICSOS algorithm is able to obtain lower root mean square error（RMSE）values and can quickly find the optimum to effectively reduce the prediction error and improve the accuracy of parameter identification.