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PDF 1.60 MB
DOI: http://dx.doi.org/10.7551/978-0-262-33027-5-ch041
Pages 207–214
First published 20 July 2015

CriPS: Critical Particle Swarm Optimisation

Adam Erskine and J. Michael Herrmann

Abstract

Particle Swarm Optimisation (PSO) is a metaheuristic used to solve search tasks and is inspired by the flocking behaviour of birds. Traditionally careful tuning of parameters are required to avoid stagnation. Many animals forage using search strategies that show power law distributions in their motions in the form of Lévy flight random walks. It might be expected that when exploring spaces for optima in the absence of any prior knowledge a similar strategy may be useful. Using feedback from swarm metrics, we engineer modifications to the standard PSO algorithm that induce criticality. Such dynamics show long tail distributions in system event sizes. The presence of large (though few) exploratory steps removes the risk of stagnation. The Critical Particle Swarm (CriPS) can be easily combined with many existing PSO extensions.