Particle swarm optimization (PSO). A tutorial

Swarm-based algorithms emerged as a powerful family of optimization techniques, inspired by the collective behavior of social animals. In particle swarm optimization (PSO) the set of candidate solutions to the optimization problem is defined as a swarm of particles which may flow through the paramet...

Full description

Saved in:
Bibliographic Details
Published inChemometrics and intelligent laboratory systems Vol. 149; pp. 153 - 165
Main Authors Marini, Federico, Walczak, Beata
Format Journal Article
LanguageEnglish
Published Elsevier B.V 15.12.2015
Subjects
Continue and discrete optimization
Particle swarm optimization (PSO)
Swarm intelligence
Variable selection
Warping algorithms
Continue and discrete optimization
Swarm intelligence
Warping algorithms
Particle swarm optimization (PSO)
Variable selection
Online AccessGet full text

Cover

More Information
Summary:Swarm-based algorithms emerged as a powerful family of optimization techniques, inspired by the collective behavior of social animals. In particle swarm optimization (PSO) the set of candidate solutions to the optimization problem is defined as a swarm of particles which may flow through the parameter space defining trajectories which are driven by their own and neighbors' best performances. In the present paper, the potential of particle swarm optimization for solving various kinds of optimization problems in chemometrics is shown through an extensive description of the algorithm (highlighting the importance of the proper choice of its metaparameters) and by means of selected worked examples in the fields of signal warping, estimation robust PCA solutions and variable selection.
ISSN:0169-7439
1873-3239
DOI:10.1016/j.chemolab.2015.08.020