Evaluation of genetic algorithms and simulated annealing for phase equilibrium and stability problems

Rangaiah, Gade Pandu

doi:10.1016/s0378-3812(01)00528-3

Cited by 110 publications

(79 citation statements)

References 39 publications

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“…Initialize the bat population xi (i = 1, 2, ...,n) and vi Define pulse frequency fi at xi Initialize pulse rates ri and the loudness Ai More details on PS problem formulation can be found in [19] and the characteristics of PS problems used in this study are summarized in Table 1.…”

Section: Phase Stability Analysis Problemsmentioning

confidence: 99%

On the Performance of Swarm Intelligence Optimization Algorithms for Phase Stability and Liquid-Liquid and Vapor-Liquid Equilibrium Calculations

Fateen

Bonilla-Petriciolet²

2015

Period. Polytech. Chem. Eng.

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IntroductionSoft computing techniques are popular and reliable numerical tools to solve real world optimization problems especially those involved in engineering applications. In particular, nature-inspired algorithms are a branch in the field of soft computing, which imitate processes in nature/inspired from nature. Nature-inspired computation can be classified into six categories [1]: swarm intelligence, natural evolution, biological neural network, molecular biology, immune system and biological cells. To date, several nature-inspired algorithms have been developed for solving difficult non-convex and multivariable optimization problems. In particular, the sophisticated decision making process that swarms of living organisms exhibit has inspired several of these meta-heuristics. Examples of these swarm intelligence optimization techniques are based on the decision making process of fireflies, ants, bees or birds. In general, the bio-inspired methods are quite simple to implement and use. They do not require any assumptions or transformation of the original optimization problems, do not require good starting points, can easily move out of local minima in their path to the global minimum, and can be applied with any model (i.e., black box model), yet provide a high probabilistic convergence to the global optimum. They can often locate the global optimum in modest computational time compared to deterministic optimization methods [2]. Therefore, these techniques are more advantageous compared to traditional local gradientbased and global deterministic optimization techniques.Recently, swarm intelligence optimization methods have been introduced for solving challenging global optimization problems involved in the thermodynamic modeling of phase equilibrium for chemical engineering applications [3][4][5][6][7][8]. In particular, the calculations of phase and chemical equilibrium are an essential component of all process simulators in chemical engineering. The prediction of phase behavior of a mixture involves the solution of two main thermodynamic problems: phase stability (PS) and phase equilibrium calculations (PEC). PS problems involve the determination of whether a system will remain in one phase at the given conditions or split into two or more phases. This type of problems usually precedes

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Section: Phase Stability Analysis Problemsmentioning

confidence: 99%

On the Performance of Swarm Intelligence Optimization Algorithms for Phase Stability and Liquid-Liquid and Vapor-Liquid Equilibrium Calculations

Fateen

Bonilla-Petriciolet²

2015

Period. Polytech. Chem. Eng.

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“…However, to design and optimize such separation operations, thermodynamic equilibrium conditions must be known. A severe problem causing enormous difficulties in this regard is that the number and identity of phases present at equilibrium are generally not known [46], which makes phase stability analysis obligatory. At a fixed temperature, pressure and global composition the problem is, therefore, to evaluate if the system is globally stable regarding the separation in two or more liquid phases.…”

Section: Phase Stabilitymentioning

confidence: 99%

“…Particularly, several variants of the simulated annealing method have been widely applied, and importantly studies have been performed concerning the so-called 'cooling schedule', by fixing the control parameters to the best values [13,46,58,66]. Naturally, a compromise must be made between efficiency and reliability, analyzing the probability of obtaining the global minimum within a reasonable computational effort.…”

Section: Phase Stabilitymentioning

confidence: 99%

Multilocal Programming and Applications

Pereira

Ferreira

Pinho

et al. 2013

Handbook of Optimization

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“…Figure 4 shows a flowchart of this algorithm. Until now, SA algorithm has been successfully used in several chemical engineering application (e.g., Rangaiah, 2001;BonillaPetriciolet et al, 2006;Wei-Zhong & Xi-Gang, 2009). In our work, we have used the SA subroutine of MATLAB®.…”

Section: Simulated Annealingmentioning

confidence: 99%

Evaluation of Stochastic Global Optimization Methods in the Design of Complex Distillation Configurations

Cabrera-Ruiz¹,

Miranda-Galindo²,

Segovia‐Hernández³

et al. 2011

Stochastic Optimization - Seeing the Optimal for the Uncertain

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Evaluation of genetic algorithms and simulated annealing for phase equilibrium and stability problems

Cited by 110 publications

References 39 publications

On the Performance of Swarm Intelligence Optimization Algorithms for Phase Stability and Liquid-Liquid and Vapor-Liquid Equilibrium Calculations

On the Performance of Swarm Intelligence Optimization Algorithms for Phase Stability and Liquid-Liquid and Vapor-Liquid Equilibrium Calculations

Multilocal Programming and Applications

Evaluation of Stochastic Global Optimization Methods in the Design of Complex Distillation Configurations

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