Photovoltaic models are both nonlinear and multimodal, and traditional algorithms are prone to fall into local optimality and insufficient recognition accuracy when identifying their parameters. In this paper, an improved artificial ecosystem optimization (IAEO) algorithm is proposed to balance exploration and exploitation by introducing a nonlinear control parameter adjustment strategy to enhance the exploration capability of the algorithm by exploiting the ergodic and non-repetitive nature of chaos. Simulation experiments show that the parameter identification accuracy of the improved algorithm exceeds 99.9% on both single, dual and triple diode and PV module models, and the RMSE value is improved by 5.5% on average on the four models compared to the original algorithm, which has a strong advantage compared to the five advanced algorithms. The improved algorithm still maintains high accuracy and stability in different environments when tested under different lighting and temperature conditions using real manufacturer data for three types of PV modules: thin-film, mono-crystalline and multi-crystalline.