Stochastic stability of particle swarm optimisation

Adam Erskine, Thomas Joyce, J. Michael Herrmann

Research output: Contribution to journalArticlepeer-review


Particle swarm optimisation (PSO) is a metaheuristic algorithm used to find good solutions in a wide range of optimisation problems. The success of metaheuristic approaches is often dependent on the tuning of the control parameters. As the algorithm includes stochastic elements that effect the behaviour of the system, it may be studied using the framework of random dynamical systems (RDS). In PSO, the swarm dynamics are quasi-linear, which enables an analytical treatment of their stability. Our analysis shows that the region of stability extends beyond those predicted by earlier approximate approaches. Simulations provide empirical backing for our analysis and show that the best performance is achieved in the asymptotic case where the parameters are selected near the margin of instability predicted by the RDS approach.
Original languageEnglish
Pages (from-to)295–315
Number of pages21
JournalSwarm Intelligence
Issue number3-4
Early online date9 Nov 2017
Publication statusPublished - 1 Dec 2017


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