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Parameter estimation in thermodynamic models has been of great interest in chemical engineering because of its complex nature, including nonlinearity, flat objective function in the neighborhood of the global optimum, badly scaled model, and nondifferential term(s) in the equations. In this article, a stochastic global optimization algorithm called integrated differential evolution (IDE) is introduced to solve the parameter estimation problems for modeling vapor–liquid equilibrium (VLE) data. In IDE, the choice of mutation strategy and associated parameters are adapted according to the learning experience from previous generations. The tabu list used in IDE can avoid revisiting the same area and avoid unnecessary function evaluations. A novel and effective stopping criterion based on the number of rejected points during the generation of a trial vector is tested and compared with other criteria. Furthermore, IDE uses a local optimizer after the global search to find the optimum accurately and efficiently. The performance of IDE for benchmark functions and VLE modeling is compared with that of other stochastic algorithms such as DE, DE with tabu list, particle swarm optimization, simulated annealing, and the deterministic algorithm Branch and Reduce Optimization Navigator (BARON). IDE is shown to be better than or comparable to these algorithms for parameter estimation in modeling VLE data. [ABSTRACT FROM AUTHOR]