Encyclopedia of Machine Learning

2010 Edition
| Editors: Claude Sammut, Geoffrey I. Webb

Particle Swarm Optimization

Reference work entry
DOI: https://doi.org/10.1007/978-0-387-30164-8_630

The Canonical Particle Swarm

The particle swarm is a population-based stochastic algorithm for optimization which is based on social–psychological principles. Unlike evolutionary algorithms, the particle swarm does not use selection; typically, all population members survive from the beginning of a trial until the end. Their interactions result in iterative improvement of the quality of problem solutions over time.

A numerical vector of D dimensions, usually randomly initialized in a search space, is conceptualized as a point in a high-dimensional Cartesian coordinate system. Because it moves around the space testing new parameter values, the point is well described as a particle. Because a number of them (usually 10 < N < 100) perform this behavior simultaneously, and because they tend to cluster together in optimal regions of the search space, they are referred to as a particle swarm.

Besides moving in a (usually) Euclidean problem space, particles are typically enmeshed in a...

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© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.U.S. Bureau of Labor StatisticsWashingtonUSA