Novel bare-bones particle swarm optimization and its performance for modeling vapor–liquid equilibrium data

Zhang, Haibo; Kennedy, Devid D.; Rangaiah, Gade Pandu; Bonilla-Petriciolet, Adrián

doi:10.1016/j.fluid.2010.10.025

Cited by 56 publications

(22 citation statements)

References 17 publications

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“…In the latter case, the swarm converges to non-optimal points [24][25][26][27]. Considering the definition of updating distribution, the best particle of each iteration remains unchanged during that iteration, which may result in stagnation.…”

Section: Introductionmentioning

confidence: 99%

“…Considering the definition of updating distribution, the best particle of each iteration remains unchanged during that iteration, which may result in stagnation. BBPSO with mutation and crossover operations (BBPSO-MC) handles this issue by employing the mutation strategy of differential evolution algorithms in the update rule of the best particle [24]. Other mutation strategies like Gaussian or Cauchy are also examined for improving bare bones PSO [25,28,29].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multi swarm bare bones particle swarm optimization with distribution adaption

Vafashoar

Meybodi

2016

Applied Soft Computing

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi swarm bare bones particle swarm optimization with distribution adaption

Vafashoar

Meybodi

2016

Applied Soft Computing

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“…Krohling and Mendel (2009) combined BPSO with Gaussian or Cauchy jumping when no fitness improvement is observed. Zhang et al (2011) proposed a enhanced BPSO with mutation and crossover operators of differential evolution algorithm to update certain particles in the population. Hybridized BPSO with differential evolution is also proposed in Omran et al (2009).…”

Section: Introductionmentioning

confidence: 99%

Integrating opposition-based learning into the evolution equation of bare-bones particle swarm optimization

Líu

Ding

et al. 2014

Soft Comput

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Bare-bones particle swarm optimization (BPSO) is attractive since it is parameter free and easy to implement. However, it suffers from premature convergence because of quickly losing diversity, and the dimensionality of the solved problems has great impact on the solution accuracy. To overcome these drawbacks, this paper proposes an oppositionbased learning (OBL) modified strategy. First, to decrease the complexity of algorithm, OBL is not used for population initialization. Second, OBL is employed on the personal best positions (i.e., Pbest) to reconstruct Pbest, which is helpful to enhance convergence speed. Finally, we choose the global worst particle (Gworst) from Pbest, which simulates the human behavior and is called rebel learning item, and is integrated into the evolution equation of BPSO to help jump out local optima by changing the flying direction. The proposed modified BPSO is called BPSO-OBL, it has been evaluated on a set of well-known nonlinear benchmark functions in different dimensional search space, and compared with several variants of BPSO, PSOs and other evolutionary algorithms. Experimental results and statistic analysis confirm promising performance of BPSO-OBL on solution accuracy and convergence speed in solving majority nonlinear functions.

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“…Lazzus et al [24] proposed PSO for modeling the phase equilibrium of complex mixtures. Zhang et al [33,34] introduced PSO algorithm for phase equilibrium calculations and for modeling vapor-liquid equilibrium data. BonillaPetriciolet, et al [35] proposed a comparative study of PSO and several of its variants for solving phase stability and equilibrium problems.…”

Section: Introductionmentioning

confidence: 99%

Prediction of CO₂ Solubility in Polymers by Radial Basis Function Artificial Neural Network Based on Chaotic Self‐adaptive Particle Swarm Optimization and Fuzzy Clustering Method

Yan¹,

Liu²,

Li³

et al. 2013

Chin. J. Chem.

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To replace costly and time-consuming experimentation in laboratory, a novel solubility prediction model based on chaos theory, self-adaptive particle swarm optimization (PSO), fuzzy c-means clustering method, and radial basis function artificial neural network (RBF ANN) is proposed to predict CO 2 solubility in polymers, hereafter called CSPSO-FC RBF ANN. The premature convergence problem is overcome by modifying the conventional PSO using chaos theory and self-adaptive inertia weight factor. Fuzzy c-means clustering method is used to tune the hidden centers and radial basis function spreads. The modified PSO algorithm is employed to optimize the RBF ANN connection weights. Then, the proposed CSPSO-FC RBF ANN is used to investigate solubility of CO 2 in polystyrene (PS), polypropylene (PP), poly(butylene succinate) (PBS) and poly(butylene succinate-co-adipate) (PBSA), respectively. Results indicate that CSPSO-FC RBF ANN is an effective method for gas solubility in polymers. In addition, compared with conventional RBF ANN and PSO ANN, CSPSO-FC RBF ANN shows better performance. The values of average relative deviation (ARD), squared correlation coefficient (R 2 ) and standard deviation (SD) are 0.1071, 0.9973 and 0.0108, respectively. Statistical data demonstrate that CSPSO-FC RBF ANN has excellent prediction capability and high-accuracy, and the correlation between prediction values and experimental data is good.

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Novel bare-bones particle swarm optimization and its performance for modeling vapor–liquid equilibrium data

Cited by 56 publications

References 17 publications

Multi swarm bare bones particle swarm optimization with distribution adaption

Multi swarm bare bones particle swarm optimization with distribution adaption

Integrating opposition-based learning into the evolution equation of bare-bones particle swarm optimization

Prediction of CO₂ Solubility in Polymers by Radial Basis Function Artificial Neural Network Based on Chaotic Self‐adaptive Particle Swarm Optimization and Fuzzy Clustering Method

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Novel bare-bones particle swarm optimization and its performance for modeling vapor–liquid equilibrium data

Cited by 56 publications

References 17 publications

Multi swarm bare bones particle swarm optimization with distribution adaption

Multi swarm bare bones particle swarm optimization with distribution adaption

Integrating opposition-based learning into the evolution equation of bare-bones particle swarm optimization

Prediction of CO2 Solubility in Polymers by Radial Basis Function Artificial Neural Network Based on Chaotic Self‐adaptive Particle Swarm Optimization and Fuzzy Clustering Method

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Prediction of CO₂ Solubility in Polymers by Radial Basis Function Artificial Neural Network Based on Chaotic Self‐adaptive Particle Swarm Optimization and Fuzzy Clustering Method