The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Particle swarm optimization (PSO). A tutorial
Open accessAbstract
Abstract 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.
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Cites
Huber, P. J. (1985) · The Annals of Statistics · doi:10.1214/aos/1176349519 · Cited by 1,277
The multivariate L 1 -median and associated data depth
Vardi, Y., & Zhang, C.-H. (2000) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.97.4.1423 · Cited by 430
Genetic algorithms in chemometrics and chemistry: a review
Leardi, R. (2001) · Journal of Chemometrics · doi:10.1002/cem.651 · Cited by 397
Cited by
Methods for state of health estimation for lithium-ion batteries: An essential review
Rhdifa, H., Ammar, A., & Bouattane, O. (2025) · E3S Web of Conferences · doi:10.1051/e3sconf/202560100071 · Cited by 2
Metadata
- Authors
- Marini, F., & Walczak, B.
- Venue
- Chemometrics and Intelligent Laboratory Systems
- Publisher
- Elsevier BV
- Published
- 2015-12
- Volume
- 149
- Pages
- 153-165
- ISSN
- 0169-7439 (ISSN-L)
- OpenAlex
- W1786686177
- Cited by
- 1,828
- References
- 3
- FWCI
- 42.54
- Type
- journal article
- Language
- en
- Open access
- green
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