Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
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
Proceedings of the 9th annual conference on Genetic and evolutionary computation
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
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
Human body pose estimation with particle swarm optimisation
Evolutionary Computation
Swarm intelligence theory: A snapshot of the state of the art
Theoretical Computer Science
A dynamic neighborhood learning based particle swarm optimizer for global numerical optimization
Information Sciences: an International Journal
Particle swarm optimisation: time for uniformisation
International Journal of Computing Science and Mathematics
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For stochastic optimisation algorithms, knowing the probability distribution with which an algorithm allocates new samples in the search space is very important, since this explains how the algorithm really works and is a prerequisite to being able to match algorithms to problems. This is the only way to beat the limitations highlighted by the no-free lunch theory. Yet, the sampling distribution for velocity-based particle swarm optimisers has remained a mystery for the whole of the first decade of PSO research. In this paper, a method is presented that allows one to exactly determine all the characteristics of a PSO's sampling distribution and explain how it changes over time during stagnation (i.e., while particles are in search for a better personal best) for a large class of PSO's.