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The Scientific World Journal
Volume 2014, Article ID 374510, 12 pages
Research Article

On the Effectiveness of Nature-Inspired Metaheuristic Algorithms for Performing Phase Equilibrium Thermodynamic Calculations

1Department of Chemical Engineering, Cairo University, Giza 12316, Egypt
2Department of Chemical Engineering, Aguascalientes Institute of Technology, 20256 Aguascalientes, AGS, Mexico

Received 27 March 2014; Accepted 30 April 2014; Published 20 May 2014

Academic Editor: Xin-She Yang

Copyright © 2014 Seif-Eddeen K. Fateen and Adrian Bonilla-Petriciolet. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The search for reliable and efficient global optimization algorithms for solving phase stability and phase equilibrium problems in applied thermodynamics is an ongoing area of research. In this study, we evaluated and compared the reliability and efficiency of eight selected nature-inspired metaheuristic algorithms for solving difficult phase stability and phase equilibrium problems. These algorithms are the cuckoo search (CS), intelligent firefly (IFA), bat (BA), artificial bee colony (ABC), MAKHA, a hybrid between monkey algorithm and krill herd algorithm, covariance matrix adaptation evolution strategy (CMAES), magnetic charged system search (MCSS), and bare bones particle swarm optimization (BBPSO). The results clearly showed that CS is the most reliable of all methods as it successfully solved all thermodynamic problems tested in this study. CS proved to be a promising nature-inspired optimization method to perform applied thermodynamic calculations for process design.