Critical Parameters in Particle Swarm Optimisation
Neural and Evolutionary Computing
2015-11-20 v1
Abstract
Particle swarm optimisation is a metaheuristic algorithm which finds reasonable solutions in a wide range of applied problems if suitable parameters are used. We study the properties of the algorithm in the framework of random dynamical systems which, due to the quasi-linear swarm dynamics, yields analytical results for the stability properties of the particles. Such considerations predict a relationship between the parameters of the algorithm that marks the edge between convergent and divergent behaviours. Comparison with simulations indicates that the algorithm performs best near this margin of instability.
Cite
@article{arxiv.1511.06248,
title = {Critical Parameters in Particle Swarm Optimisation},
author = {J. Michael Herrmann and Adam Erskine and Thomas Joyce},
journal= {arXiv preprint arXiv:1511.06248},
year = {2015}
}
Comments
9 pages, 5 figures