Prof Peter Andras P.Andras@napier.ac.uk
Dean of School of Computing Engineering and the Built Environment
Prof Peter Andras P.Andras@napier.ac.uk
Dean of School of Computing Engineering and the Built Environment
Particle swarm optimization is a popular method for solving difficult optimization problems. There have been attempts to formulate the method in formal probabilistic or stochastic terms (e.g. bare bones particle swarm) with the aim to achieve more generality and explain the practical behavior of the method. Here we present a Bayesian interpretation of the particle swarm optimization. This interpretation provides a formal framework for incorporation of prior knowledge about the problem that is being solved. Furthermore, it also allows to extend the particle optimization method through the use of kernel functions that represent the intermediary transformation of the data into a different space where the optimization problem is expected to be easier to be resolved–such transformation can be seen as a form of prior knowledge about the nature of the optimization problem. We derive from the general Bayesian formulation the commonly used particle swarm methods as particular cases.
Andras, P. (2012). A Bayesian interpretation of the particle swarm optimization and its kernel extension. PLOS ONE, 7(11), Article e48710. https://doi.org/10.1371/journal.pone.0048710
Journal Article Type | Article |
---|---|
Acceptance Date | Oct 3, 2012 |
Online Publication Date | Nov 7, 2012 |
Publication Date | Nov 7, 2012 |
Deposit Date | Nov 2, 2021 |
Publicly Available Date | Nov 2, 2021 |
Journal | PLOS ONE |
Print ISSN | 1932-6203 |
Publisher | Public Library of Science |
Peer Reviewed | Peer Reviewed |
Volume | 7 |
Issue | 11 |
Article Number | e48710 |
DOI | https://doi.org/10.1371/journal.pone.0048710 |
Public URL | http://researchrepository.napier.ac.uk/Output/2808904 |
A Bayesian Interpretation Of The Particle Swarm Optimization And Its Kernel Extension
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This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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