Swarm intelligence
The particle swarm optimization algorithm: convergence analysis and parameter selection
Information Processing Letters
Distributed computing in practice: the Condor experience: Research Articles
Concurrency and Computation: Practice & Experience - Grid Performance
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
MALLBA: a software library to design efficient optimisation algorithms
International Journal of Innovative Computing and Applications
Handbook of Parametric and Nonparametric Statistical Procedures
Handbook of Parametric and Nonparametric Statistical Procedures
Neighborhood re-structuring in particle swarm optimization
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
The fully informed particle swarm: simpler, maybe better
IEEE Transactions on Evolutionary Computation
Why six informants is optimal in PSO
Proceedings of the 14th annual conference on Genetic and evolutionary computation
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In the standard particle swarm optimization (PSO), a new particle's position is generated using two main informant elements: the best position the particle has found so far and the best performer among its neighbors. In fully informed PSO, each particle is influenced by all the remaining ones in the swarm, or by a series of neighbors structured in static topologies (ring, square, or clusters). In this paper, we generalize and analyze the number of informants that take part in the calculation of new particles. Our aim is to discover if a quasi-optimal number of informants exists for a given problem. The experimental results seem to suggest that 6 to 8 informants could provide our PSO with higher chances of success in continuous optimization for well-known benchmarks.