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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Kennedy, J. (2017). Particle Swarm Optimization. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7687-1_630
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