Particle swarm optimization (PSO). A tutorial

Eberhart, 2001

Beni, 1989, Swarm Intelligence, 425

Engelbrecht, 2005

Bonabeau, 1999

2008

Leardi, 2001, Genetic algorithms in chemometrics and chemistry: a review, J. Chemometr., 15, 559, 10.1002/cem.651

1995

Yang, 2014

Dorigo, 1992

Dorigo, 2004

Kennedy, 1995, Particle swarm optimization, vol.4, 1942

Meissner, 2006, Optimized Particle Swarm Optimization (OPSO) and its application to artificial neural network training, BMC Bioinformatics, 7, 125, 10.1186/1471-2105-7-125

Mohammadi, 2014, Comparison of particle swarm optimization and backpropagation algorithms for training feedforward neural network, J. Math. Computer Sci., 12, 113, 10.22436/jmcs.012.02.03

Hadavandi, 2010, Developing a time series model based on particle swarm optimization for gold price forecasting, 337

Yang, 2011, Accelerated particle swarm optimization and support vector machine for business optimization and applications, 53

Schwaab, 2008, Nonlinear parameter estimation through particle swarm optimization, Chem. Eng. Sci., 63, 1542, 10.1016/j.ces.2007.11.024

Marini, 2011, Finding relevant clustering directions in high-dimensional data using particle swarm optimization, J. Chemometr., 25, 366, 10.1002/cem.1345

Sorol, 2010, Visible/near infrared-partial least-squares analysis of Brix in sugar cane juice. A test field for variable selection methods, Chemometr. Intell. Lab. Syst., 102, 100, 10.1016/j.chemolab.2010.04.009

Parastar, 2013, Multivariate curve resolution-particle swarm optimization: a high-throughput approach to exploit pure information from multi-component hyphenated chromatographic signals, Anal. Chim. Acta, 772, 16, 10.1016/j.aca.2013.02.042

Beyramysoltan, 2013, Investigation of the equality constraint effect on the reduction of the rotational ambiguity in three-component system using a novel grid search method, Anal. Chim. Acta, 791, 25, 10.1016/j.aca.2013.06.043

Skvortsov, 2014, Estimation of rotation ambiguity in multivariate curve resolution with charged particle swarm optimization (cPSO-MCR), J. Chemometr., 28, 727, 10.1002/cem.2663

Fan, 2009, Process identification using a new component analysis model and particle swarm optimization, Chemometr. Intell. Lab. Syst., 99, 19, 10.1016/j.chemolab.2009.07.006

Krause, 2015, Online monitoring of bioprocesses via multivariate sensor prediction within swarm intelligence decision making, Chemometr. Intell. Lab. Syst., 145, 48, 10.1016/j.chemolab.2015.04.012

Shen, 2004, Modified particle swarm optimization algorithm for variable selection in MLR and PLS modeling: QSAR studies of antagonism of angiotensin II antagonists, Eur. J. Pharm. Sci., 22, 145, 10.1016/j.ejps.2004.03.002

Cernuda, 2013, Hybrid evolutionary particle swarm optimization and ant colony optimization for variable selection, vol. 3, 7

Luo, 2013, Adaptive configuration of radial basis function network by regression tree allied with hybrid particle swarm optimization algorithm, Chemometr. Intell. Lab. Syst., 124, 50, 10.1016/j.chemolab.2013.02.002

Li, 2014, Particle swarm optimization-based protocol for partial least-squares discriminant analysis: application to 1H nuclear magnetic resonance analysis of lung cancer metabonomics, Chemometr. Intell. Lab. Syst., 135, 192, 10.1016/j.chemolab.2014.04.014

Fletcher, 2000

Pearl, 1984

Edelkamp, 2011

Shi, 1998, Parameter selection in particle swarm optimization, 591

van den Bergh, 2001

Clerc, 2002, The particle swarm: explosion stability and convergence in a multi-dimensional complex space, IEEE Trans. Evolution. Comput., 6, 58, 10.1109/4235.985692

Engelbrecht, 2012, Particle swarm optimization: velocity initialization, 1

Ozcan, 1998, Analysis of a simple particle swarm optimization system, vol. 8, 253

Eberhart, 2000, Comparing inertia weights and constriction factors in particle swarm optimization, vol.1, 84

Shi, 1998, A modified particle swarm optimizer, vol.1, 69

Bansal, 2011, Inertia weight strategies in particle swarm optimization, 640

Eberhart, 2001, Tracking and optimizing dynamic systems with particle swarms, vol.1, 94

Xin, 2009, A particle swarm optimizer with multistage linearly-decreasing inertia weight, vol.1, 505

Arumugam, 2006, On the performance of the particle swarm optimization algorithm with various inertia weight variants for computing optimal control of a class of hybrid systems, Discrete Dyn. Nat. Soc., 2006, 79295, 10.1155/DDNS/2006/79295

Feng, 2007, Chaotic Inertia Weight in Particle Swarm Optimization, 475

Muñoz Zavala, 2013, A comparison study of PSO neighborhoods, 251

Kennedy, 1997, A discrete binary version of the particle swarm algorithm, vol. 5, 4104

Ackley, 1987

Bäck, 1996

Malmquist, 1994, Alignment of chromatographic profiles for principal component analysis: a prerequisite for fingerprinting methods, J. Chromatogr. A, 687, 71, 10.1016/0021-9673(94)00726-8

Kassidas, 1998, Synchronization of batch trajectories using dynamic time warping, AIChE J., 44, 864, 10.1002/aic.690440412

Vest Nielsen, 1998, Aligning of single and multiple wavelength chromatographic profiles for chemometric data analysis using correlation optimised warping, J. Chromatogr. A, 805, 17, 10.1016/S0021-9673(98)00021-1

Eilers, 2004, Parametric time warping, Anal. Chem., 76, 404, 10.1021/ac034800e

Johnson, 2003, High-speed peak matching algorithm for retention time alignment of gas chromatographic data for chemometric analysis, J. Chromatogr. A, 996, 141, 10.1016/S0021-9673(03)00616-2

Daszykowski, 2010, Automated alignment of one-dimensional chromatographic signals, J. Chromatogr. A, 1217, 6127, 10.1016/j.chroma.2010.08.008

Savorani, 2010, icoshift: a versatile tool for the rapid alignment of 1D NMR spectra, J. Magn. Reson., 202, 190, 10.1016/j.jmr.2009.11.012

Tomasi, 2011, icoshift: an effective tool for the alignment of chromatographic data, J. Chromatogr. A, 1218, 7832, 10.1016/j.chroma.2011.08.086

Walczak, 2005, Fuzzy warping of chromatograms, Chemometr. Intell. Lab. Syst., 77, 173, 10.1016/j.chemolab.2004.07.012

Daszykowski, 2007, Identifying potential biomarkers in LC–MS, J. Chemometr., 21, 292, 10.1002/cem.1066

Skov, 2008, Multiblock variance partitioning. a new approach for comparing variation in multiple data blocks, Anal. Chim. Acta, 615, 18, 10.1016/j.aca.2008.03.045

Croux, 1996, A fast algorithm for robust principal components based on projection pursuit, 211

Friedman, 1974, A projection pursuit algorithm for exploratory data analysis, IEEE Trans. Comp., C-23, 881, 10.1109/T-C.1974.224051

Huber, 1985, Projection pursuit, Ann. Stat., 13, 435, 10.1214/aos/1176349519

Maronna, 2006

Rousseouw, 1993, Alternatives to the median absolute deviation, J. Am. Stat. Assoc., 88, 1273, 10.1080/01621459.1993.10476408

Vardi, 2000, The multivariate L-1 median and associated data depth, Proc. Nat. Acad. Sci., 97, 1423, 10.1073/pnas.97.4.1423

Croux, 2007, Algorithms for projection-pursuit robust principal component analysis, Chemometr. Intell. Lab. Syst., 87, 218, 10.1016/j.chemolab.2007.01.004

Hawkins, 1984, Location of several outliers in multiple regression data using elemental sets, Technometrics, 26, 197, 10.1080/00401706.1984.10487956

Hubert, 2005, ROBPCA: a new approach to robust principal components analysis, Technometrics, 47, 64, 10.1198/004017004000000563

Martens, 1989

Leardi, 2003, Genetic algorithms-PLS as a tool for wavelength selection in spectral data sets, 169

Bellato, 2011, Use of near infrared reflectance and transmittance coupled to robust calibration for the evaluation of nutritional value in naked oats, J. Agric. Food Chem., 59, 4349, 10.1021/jf200087y

Guo, 2008, A novel LS-SVMs hyper-parameter selection based on particle swarm optimization, Neurocomputing, 71, 3211, 10.1016/j.neucom.2008.04.027