Gravitational Search, Monkey, and Krill Herd Swarm Algorithms for Phase Stability, Phase Equilibrium, and Chemical Equilibrium Problems

Phase equilibrium calculations (PECs) and phase stability (PS) analysis of reactive and nonreactive systems problems are important for the simulation and design of chemical engineering processes. These problems, which are challenging, multi-variable, and non-convex, require optimization techniques t...

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Bibliographic Details
Published in:Chemical engineering communications 2016-03, Vol.203 (3), p.389-406
Main Authors: Khalil, Ahmed M. E., Fateen, Seif-Eddeen K., Bonilla-Petriciolet, Adrian
Format: Article
Language:English
Subjects:
Algorithms
Chemical engineering
Equilibrium
Global optimization
Gravitation
Gravitational search algorithm
Heuristic methods
Krill
Krill Herd algorithm
Mathematical models
Monkey algorithm
Monkeys
Optimization techniques
Phase and chemical equilibrium calculations
Phase equilibria
Phase stability
Phase stability analysis
Search algorithms
Searching
Stability analysis
Stochastic swarm algorithms
Stochasticity
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Summary:Phase equilibrium calculations (PECs) and phase stability (PS) analysis of reactive and nonreactive systems problems are important for the simulation and design of chemical engineering processes. These problems, which are challenging, multi-variable, and non-convex, require optimization techniques that are both efficient and effective in finding the solution. Stochastic global optimization algorithms, especially swarm algorithms, are promising tools for such problems. In this study, monkey algorithm (MA), gravitational search algorithm (GSA), and Krill Herd algorithm (KHA) were used to solve PS, phase equilibrium, and chemical equilibrium problems. We have also studied the effect of adding a local optimizer at the end of the stochastic optimizer run. The results were compared to determine the strengths and weaknesses of each algorithm. When a local optimizer was used, MA was found to be a reliable algorithm in solving the problems. GSA had relatively the least numerical effort for all problems among the three algorithms but with low reliability. KHA was more reliable than other two algorithms without the use of a local optimizer. The performance of GSA, MA, and KHA was compared with firefly algorithm and cuckoo search (CS). In summary, this study found that CS algorithm was more reliable than the newly tested algorithms. Nevertheless, MA and GSA algorithms, when combined with a local optimizer, solve the thermodynamic problems as reliably and efficiently as CS.
ISSN:0098-6445
1563-5201
DOI:10.1080/00986445.2015.1004666