Particle swarm optimization with an oscillating inertia weight

Authors:
Kyriakos Kentzoglanakis;Matthew Poole
Affiliations:
University of Portsmouth, Portsmouth, United Kingdom;University of Portsmouth, Portsmouth, United Kingdom
Venue:
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Year:
2009

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Abstract

In this paper, we propose an alternative strategy of adapting the inertia weight parameter during the course of particle swarm optimization, by means of a non-monotonic inertia weight function of time. Results demonstrate that an oscillating inertia weight function is competitive and in some cases better than established inertia weight functions, in terms of consistency and speed of convergence.