Exact analysis of the sampling distribution for the canonical particle swarm optimiser and its convergence during stagnation

Several theoretical analyses of the dynamics of particle swarms have been offered in the literature over the last decade. Virtually all rely on substantial simplifications, including the assumption that the particles are deterministic. This has prevented the exact characterisation of the sampling distribution of the PSO. In this paper we introduce a novel method, which allows one to exactly determine all the characteristics of a PSO’s sampling distribution and explain how they change over any number of generations, in the presence stochasticity. The only assumption we make is stagnation, i.e., we study the sampling distribution produced by particles in search for a better personal best. We apply the analysis to the PSO with inertia weight, but the analysis is also valid for the PSO with constriction. Categories and Subject Descriptors I.2.8 [Artificial Intelligence]: Problem Solving, Control Methods, and Search General Terms Performance Keywords Particle Swarm Optimisation, Theor...

• Type: Conference
• Year: 2007
• Where: GECCO
• Authors: Riccardo Poli, David S. Broomhead
• Keywords: GECCO 2007, Particle Swarm, PSO’s Sampling Distribution, Sampling Distribution