In this work a particle swarm optimization (PSO) algorithm was used to fit an analytic potential energy function to I-(H2O) intermolecular potential energy curves calculated with DFT/B97-1 theory. The analytic function is a sum of two-body terms, each written as a generalized sum of Buckingham and Lennard-Jones terms with only six parameters. Two models were used to describe the two-body terms between I- and H2O: a three-site model H2O and a four-site model including a ghost atom. The fits are compared with those obtained with a genetic/nonlinear least-squares algorithm. The ghost atom model significantly improves the fitting accuracy for both algorithms. The PSO fits are significantly more accurate and much less time-consuming than those obtained with the genetic/nonlinear least-squares algorithm. Eight I- - -H2O potential energy curves, fit with the PSO algorithm for the three- and four-site models, have RMSE of 1.37 and 0.22 kcal/mol and compute times of ∼20 and ∼68 min, respectively. The PSO fit for the four-site model is quite adequate for determining densities of states and partition functions for I-(H2O)n clusters at high energies and temperatures, respectively. The PSO algorithm was also applied to the eight potential energy curves, with the four-site model, for a short time ∼8 min fitting. The RMSE was small, only 0.37 kcal/mol, showing the high efficiency of the PSO algorithm with retention of a good fitting accuracy. The PSO algorithm is a good choice for fitting analytic potential energy functions, and for the work presented here was able to find an adequate fit to an I-(H2O) analytic intermolecular potential with a small number of parameters.