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Particle swarm optimization almost surely finds local optima

Particle swarm optimization (PSO) is a popular nature-inspired meta-heuristic for solving continuous optimization problems. Although this technique is widely used, up to now only some partial aspects of the method have been formally investigated. In particular, while it is well-studied how to let th...

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Bibliographic Details
Published in:Theoretical computer science 2015, Vol.561, p.57-72
Main Authors: Schmitt, Manuel, Wanka, Rolf
Format: Article
Language:English
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Summary:Particle swarm optimization (PSO) is a popular nature-inspired meta-heuristic for solving continuous optimization problems. Although this technique is widely used, up to now only some partial aspects of the method have been formally investigated. In particular, while it is well-studied how to let the swarm converge to a single point in the search space, no general theoretical statements about this point or on the best position any particle has found have been known. For a very general class of objective functions, we provide for the first time results about the quality of the solution found. We show that a slightly adapted PSO almost surely finds a local optimum. To do so, we investigate the newly defined potential of the swarm. The potential drops when the swarm approaches the point of convergence, but increases if the swarm remains close to a point that is not a local optimum, meaning that the swarm charges potential and continues its movement.
ISSN:0304-3975
1879-2294
DOI:10.1016/j.tcs.2014.05.017