Particle swarm optimization (PSO) is applied to fit the experimental data to the theoretical thermodynamic model. The PSO technique was developed by Kennedy and Eberhart [2, 3]. PSO algorithms mimic the social behavior patterns of organisms that live and interact within large groups, such as swarms of bees. You can vary the number of iterations as well as the swarm size to change the fitting quality. However, notice the complementary effect of these two parameters. For every binary mixture, the isobaric VLE diagram and the VLE curve (continuous lines) at are generated and compared to the experimental data shown using the filled squares.