A Particle Swarm Optimization-Based Algorithm for Job-Shop Scheduling Problems

Ge, Hongwei; Zhou, Yuan; Guo, Xuexin

doi:10.1142/s0219876205000569

Cited by 29 publications

(8 citation statements)

References 20 publications

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“…The LS-SVM method has the capability for linear and nonlinear multivariate calibration, and the PSO algorithm has been found to be fast and robust in solving nonlinear and nondi®erentiable problems. 29 Therefore, the PSObased LS-SVM method is more appropriate to handle the NIR spectral data of complex TCM solution in this study.…”

Section: Comparison Of Four Regression Methodsmentioning

confidence: 99%

Application of particle swarm optimization-based least square support vector machine in quantitative analysis of extraction solution of yangxinshi tablet using near infrared spectroscopy

Lou

Yang

Zhu

et al. 2014

J. Innov. Opt. Health Sci.

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A particle swarm optimization (PSO)-based least square support vector machine (LS-SVM) method was investigated for quantitative analysis of extraction solution of Yangxinshi tablet using near infrared (NIR) spectroscopy. The usable spectral region (5400-6200 cm À1 ) was identi¯ed, then the¯rst derivative spectra smoothed using a Savitzky-Golay¯lter were employed to establish calibration models. The PSO algorithm was applied to select the LS-SVM hyperparameters (including the regularization and kernel parameters). The calibration models of total°a vonoids, puerarin, salvianolic acid B and icariin were established using the optimum hyperparameters of LS-SVM. The performance of LS-SVM models were compared with partial least squares (PLS) regression, feed-forward back-propagation network (BPANN) and support vector machine (SVM). Experimental results showed that both the calibration results and prediction accuracy of the PSO-based LS-SVM method were superior to PLS, BP-ANN and SVM. For PSObased LS-SVM models, the determination coe±cients (R 2 ) for the calibration set were above 0.9881, and the RSEP values were controlled within 5.772%. For the validation set, the RMSEP values were close to RMSEC and less than 0.042, the RSEP values were under 8.778%, which This is an Open Access article published by World Scienti¯c Publishing Company. It is distributed under the terms of the Creative Commons Attribution 3.0 (CC-BY) License. Further distribution of this work is permitted, provided the original work is properly cited. Vol. 7, No. 6 (2014) were much lower than the PLS, BP-ANN and SVM models. The PSO-based LS-SVM algorithm employed in this study exhibited excellent calibration performance and prediction accuracy, which has de¯nite practice signi¯cance and application value. Journal of Innovative Optical Health Sciences

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Section: Comparison Of Four Regression Methodsmentioning

confidence: 99%

Application of particle swarm optimization-based least square support vector machine in quantitative analysis of extraction solution of yangxinshi tablet using near infrared spectroscopy

Lou

Yang

Zhu

et al. 2014

J. Innov. Opt. Health Sci.

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show abstract

“…The PSO algorithm has been found to be fast and robust in solving nonlinear, non-differentiable and multimodal problems. 42 The in-depth mathematical description and executive steps of the PSO can be found in Lin et al 43 Although the NIR data of the spectral region of interest could be applied as input for the LS-SVM model, the training time increases with the square of the number of training samples and linearly with the number of variables. 27 In order to improve the training speed and reduce the training error, scores of the first several PCs obtained from PCA were applied as input of LS-SVM models.…”

Section: Annmentioning

confidence: 99%

Rapid and Quantitative Detection Method for Acteoside during Chromatographic Purification of Adhesive Rehmannia Leaf Extract Using near Infrared Spectroscopy and Chemometrics

Jin

Liu

Luan

et al. 2013

Journal of Near Infrared Spectroscopy

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The feasibility of rapid and non-destructive analysis of acteoside, the main active component of adhesive rehmannia leaf, in the macroporous resin adsorption and elution process by Fourier transform near infrared spectroscopy together with different chemometrics methods was investigated in this study. The usable spectral region (5500-6200 cm −1) was identified, spectral preprocessing, including Savitzky-Golay smoothing derivative was employed, and spectral dimension was also reduced through principal component analysis for artificial neural networks (ANN) and least square support vector machines (LS-SVM) methods. The multivariate calibration models based on principal component regression, partial least-squares regression, ANN, and particle swarm optimisation-based LS-SVM were developed to correlate the pretreated spectral data and the corresponding acteoside concentrations determined by high performance liquid chromatography. For all regression models, the coefficients of determination for all data sets (calibration, validation and prediction set) were higher than 0.988, indicating that the four methods were effective for building acteoside calibration models using samples taken from either the adsorption or the elution process. However, according to the relative standard error of prediction values for the prediction set samples, the LS-SVM model was slightly more accurate than the models obtained using the other regression techniques.

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“…Furthermore, the PSO has also been found to be robust and fast in solving nonlinear, non-differentiable and multimodal problems [8]. The progress of PSO research and the recent achievements for applications to large-scale optimization problems are reviewed in [9].…”

Section: Introductionmentioning

confidence: 99%

AMT-PSO: An Adaptive Magnification Transformation Based Particle Swarm Optimizer

Zhang

Xie

et al. 2011

IEICE Trans. Inf. & Syst.

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SUMMARYThis paper presents an adaptive magnification transformation based particle swarm optimizer (AMT-PSO) that provides an adaptive search strategy for each particle along the search process. Magnification transformation is a simple but very powerful mechanism, which is inspired by using a convex lens to see things much clearer. The essence of this transformation is to set a magnifier around an area we are interested in, so that we could inspect the area of interest more carefully and precisely. An evolutionary factor, which utilizes the information of population distribution in particle swarm, is used as an index to adaptively tune the magnification scale factor for each particle in each dimension. Furthermore, a perturbation-based elitist learning strategy is utilized to help the swarm's best particle to escape the local optimum and explore the potential better space. The AMT-PSO is evaluated on 15 unimodal and multimodal benchmark functions. The effects of the adaptive magnification transformation mechanism and the elitist learning strategy in AMT-PSO are studied. Results show that the adaptive magnification transformation mechanism provides the main contribution to the proposed AMT-PSO in terms of convergence speed and solution accuracy on four categories of benchmark test functions.

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A Particle Swarm Optimization-Based Algorithm for Job-Shop Scheduling Problems

Cited by 29 publications

References 20 publications

Application of particle swarm optimization-based least square support vector machine in quantitative analysis of extraction solution of yangxinshi tablet using near infrared spectroscopy

Application of particle swarm optimization-based least square support vector machine in quantitative analysis of extraction solution of yangxinshi tablet using near infrared spectroscopy

Rapid and Quantitative Detection Method for Acteoside during Chromatographic Purification of Adhesive Rehmannia Leaf Extract Using near Infrared Spectroscopy and Chemometrics

AMT-PSO: An Adaptive Magnification Transformation Based Particle Swarm Optimizer

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