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.