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A Taxonomy of Heterogeneity and Dynamics in Particle Swarm Optimisation
Harry Goldingay and Peter R. Lewis
Aston Lab for Intelligent Collectives Engineering (ALICE), Aston Institute for Systems Analytics, Aston University, Birmingham, UKgoldinhj@aston.ac.uk
p.lewis@aston.ac.uk
Abstract. We propose a taxonomy for heterogeneity and dynamics of swarms in PSO, which separates the consideration of homogeneity and heterogeneity from the presence of adaptive and non-adaptive dynamics, both at the particle and swarm level. It supports research into the separate and combined contributions of each of these characteristics. An analysis of the literature shows that most recent work has focussed on only parts of the taxonomy. Our results agree with prior work that both heterogeneity, where particles exhibit different behaviour from each other at the same point in time, and dynamics, where individual particles change their behaviour over time, are useful. However while heterogeneity does typically improve PSO, this is often dominated by the improvement due to dynamics. Adaptive strategies used to generate heterogeneity may end up sacrificing the dynamics which provide the greatest performance increase. We evaluate exemplar strategies for each area of the taxonomy and conclude with recommendations.
LNCS 8672, p. 171 ff.
lncs@springer.com
© Springer International Publishing Switzerland 2014