Assessment of capabilities and limitations of stochastic global optimization methods for modeling mean activity coefficients of ionic liquids

In this paper, we have studied and analyzed the capabilities and limitations of seven stochastic global optimization methods to model mean activity coefficients of ammonium aqueous electrolytes using the electrolyte NRTL model. These stochastic methods are: simulated annealing (SA), genetic algorithm (GA), differential evolution (DE), particle swarm optimization (PSO), DE with tabu list (DETL), harmony search (HS) and bare bones PSO (BBPSO). Convergence characteristics and performances of these optimization methods have been tested and compared using different stopping conditions. Overall, our results indicate that SA, DETL and BBPSO offer better performance and promising convergence characteristics for solving parameter estimation problems involved in the modeling of thermodynamic properties of ionic liquids (ILs).