On the Performance of Swarm Intelligence Optimization Algorithms for Phase Stability and Liquid-Liquid and Vapor-Liquid Equilibrium Calculations
This study introduces new soft computing optimization techniques for performing the phase stability analysis and phase equilibrium calculations in both reactive and non-reactive systems. In particular, the performance of the several swarm intelligence optimization methods is compared and discussed based on both reliability and computational efficiency using practical stopping criteria for these applied thermodynamic calculations. These algorithms are: Intelligent Firefly Algorithm (IFA), Cuckoo Search (CS), Artificial Bee Algorithm (ABC) and Bat Algorithm (BA). It is important to note that no attempts have been reported in the literature to evaluate their performance in solving the phase and chemical equilibrium problems. Results indicated that CS was found to be the most reliable technique across different problems tried at the time that it requires similar computational effort to the other methods. In summary, this study provides new results and insights about the capabilities and limitations of bio-inspired optimization methods for performing applied thermodynamic calculations.
Keywords: Swarm intelligence, optimization methods, phase equilibrium, phase stability, chemical equilibrium
How to Cite
Fateen, S.-E. K., Bonilla-Petriciolet, A. “On the Performance of Swarm Intelligence Optimization Algorithms for Phase Stability and Liquid-Liquid and Vapor-Liquid Equilibrium Calculations”, Periodica Polytechnica Chemical Engineering, 59(3), pp. 186-200, 2015. https://doi.org/10.3311/PPch.7636