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Utilizing the numerical analysis and optimization method for extracting solar cells model parameters, one recurrent issue refers to the difficulty in initializing the parameters. Moreover, those methods using solar cells exponential model are sensible to small changes in the data measured. A chaotic particle swarm optimization algorithm (CPSO) was presented for extracting solar cell model parameters, in which the global search performance and local convergence of particle swarm optimization (PSO) were improved by introducing a chaos search. The CPSO searched for optimal parameters without strict limitation on the search ranges. The procedure is illustrated by applying it to parameters extraction using the current-voltage data measured from a silicon cell and a solar module. The results demonstrate that the method can reduce the influence of experimental data measurement accuracy, and the statistical analysis data of fitting (I-V) characteristics curves are better than that of other published methods.