.png){kind=icon}
Return
Particle swarm optimization (PSO). A tutorial
.png){kind=icon}
PRE
AI
DOI：10.1016/j.chemolab.2015.08.020.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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. (C) 2015 Elsevier B.V. All rights reserved.
Keywords:
Particle swarm optimization (PSO)
Variable selection
Warping algorithms
Continue and discrete optimization
Swarm intelligence
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.8
Papers:
4.6K
Citations:
1.2W
Organization
Cited Papers
Multiblock variance partitioning: A new approach for comparing variation in multiple data blocks
.png){kind=icon}
PREAI
ShareSave
ShareSaveShareSaveShareSave
View more