EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
The particle swarm optimization algorithm: convergence analysis and parameter selection
Information Processing Letters
Particle swarms and population diversity
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Fundamentals of Computational Swarm Intelligence
Fundamentals of Computational Swarm Intelligence
Population structure and particle swarm performance
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Population structure and particle swarm performance
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
Stability analysis of the particle dynamics in particle swarm optimizer
IEEE Transactions on Evolutionary Computation
On the moments of the sampling distribution of particle swarm optimisers
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
Dynamics and stability of the sampling distribution of particle swarm optimisers via moment analysis
Journal of Artificial Evolution and Applications - Particle Swarms: The Second Decade
Journal of Artificial Evolution and Applications - Particle Swarms: The Second Decade
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Allocation of local and global search capabilities of particle in canonical PSO
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Simple Dynamic Particle Swarms without Velocity
ANTS '08 Proceedings of the 6th international conference on Ant Colony Optimization and Swarm Intelligence
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part I
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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.