Modified particle swarm optimization algorithm for variable selection in MLR and PLS modeling: QSAR studies of antagonism of angiotensin II antagonists

Shen, Qi; Jiang, Jian‐Hui; Jiao, Chen-Xu; Shen, Guo‐Li; Yu, Ru‐Qin

doi:10.1016/j.ejps.2004.03.002

Cited by 123 publications

(34 citation statements)

References 22 publications

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“…This step is necessary to convert the continuous velocity into scaled values. Note that the update of the particle velocity can be done differently as stated in Reference [10]. In the original PSO algorithm, the velocity is updated with regard to p i , the best state for a variable found so far.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

“…In 2004, Yu et al introduced PSO for variable selection in NIRS [10,21]. PSO was developed by Kennedy and Eberhart [22,23] to mimic the behavior of social insects based on three sociocognitive underpinnings: evaluate, compare, and imitate.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

“…Many authors have stated that even though global multivariate methods were able to cope with a large part of the noise, the selection of variables could bring robustness and higher calibration performances [2,3]. These same authors have presented techniques such as genetic algorithm (GA) [4][5][6][7], simulated annealing [8], interval-PLS [9], and particle swarm optimization (PSO) [10], to extract relevant spectral features.…”

Section: Introductionmentioning

confidence: 99%

“…Leardi [20] and Koljonen [10] provided good overviews of the method with various applications and an extended list of authors having participated in the development of the approach. The success of GAs lies in the stochastic steps governing the selection of original individuals, crossovers and mutations positions, etc.…”

Section: Introductionmentioning

confidence: 99%

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Local chemometrics for samples and variables: optimizing calibration and standardization processes

Igne

Hurburgh

2010

Journal of Chemometrics

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The ability of five sample selection methods for local chemometrics and three variable selection algorithms were compared for the development and the transfer of whole soybeans protein and oil near infrared prediction models. Two new methods based on a similarity index considering Euclidian distance among Fourier coefficients were introduced and tested against more common approaches (locally weighted regression, LOCAL). Genetic algorithms were also challenged with the development of models based on particle swarm optimization (PSO). A modification to the original PSO model was introduced. Sample and variable selection methods, as well as their combinations, were tested in the transfer of models in intra-and inter-brand situations using two Foss Infratecs and two Bruins OmegAnalyzerGs. For each brand, a master was designated and its models transferred onto the second unit of its network and the two units of the second brand. Calibration models were proven transferable from brand to brand with similar or better precisions than when all instruments were calibrated on their own calibration sets (relative predictive determinant (RPD) improving from 10.42 to 12.76 and 12.39 in intra-brand standardization for Infratec network with local and variable selection methods respectively). These methods provided contrasted results depending on the instrument, the parameter, and the variability of interest.

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Section: Particle Swarm Optimizationmentioning

confidence: 99%

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Local chemometrics for samples and variables: optimizing calibration and standardization processes

Igne

Hurburgh

2010

Journal of Chemometrics

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“…In general, it max 4 v  will be able to meet the condition. In order to simplify the problem, this paper proposes an improved PSO algorithm and the random probability in [7] and [8] …”

Section: A Improved Particle Swarm Optimization Algorithmsmentioning

confidence: 99%

Forecasting of Electricity Load Based on Improved Particle Swarm Optimization and Support Vector Regression Machine

Liu¹

2015

Proceedings of the 2015 International Conference on Test, Measurement and Computational Methods

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Abstract-Support vector regression machine is suitable for small sample decision and it is good to data forecasting capabilities. Its nature of learning method is under the condition of limited information to obtain a good ability in data mining. Accurate electricity load forecasting is an important practical value to our lives. This paper presents a new algorithm that is an improved particle swarm optimization algorithm and support vector regression machine that is proposed to predict electricity load. It is of great significance to forecasting electricity load. The algorithm can optimize training parameters of support vector regression machine by improved particle swarm optimization algorithm. The simulation experimental results indicate that the new algorithm made a meaningful exploration on forecasting electricity load.

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Computational Modeling in Bioprocess Development

Insaidoo

Banerjee

Roush

et al. 2017

Preparative Chromatography for Separation of Proteins

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Modified particle swarm optimization algorithm for variable selection in MLR and PLS modeling: QSAR studies of antagonism of angiotensin II antagonists

Cited by 123 publications

References 22 publications

Local chemometrics for samples and variables: optimizing calibration and standardization processes

Local chemometrics for samples and variables: optimizing calibration and standardization processes

Forecasting of Electricity Load Based on Improved Particle Swarm Optimization and Support Vector Regression Machine

Computational Modeling in Bioprocess Development

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