Stochastic Local Search: Foundations & Applications
Stochastic Local Search: Foundations & Applications
PSO and multi-funnel landscapes: how cooperation might limit exploration
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Fundamentals of Computational Swarm Intelligence
Fundamentals of Computational Swarm Intelligence
On the moments of the sampling distribution of particle swarm optimisers
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
Neighborhood topologies in fully informed and best-of-neighborhood particle swarms
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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
Heterogeneous particle swarm optimizers
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
An exploration of topologies and communication in large particle swarms
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Frankenstein's PSO: a composite particle swarm optimization algorithm
IEEE Transactions on Evolutionary Computation
Applied Computational Intelligence and Soft Computing
Investigating particle swarm optimisation topologies for edge detection in noisy images
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
Why six informants is optimal in PSO
Proceedings of the 14th annual conference on Genetic and evolutionary computation
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The fully informed particle swarm optimization algorithm (FIPS) is very sensitive to changes in the population topology. The velocity update rule used in FIPS considers all the neighbors of a particle to update its velocity instead of just the best one as it is done in most variants. It has been argued that this rule induces a random behavior of the particle swarm when a fully connected topology is used. This argument could explain the often observed poor performance of the algorithm under that circumstance. In this paper we study experimentally the convergence behavior of the particles in FIPS when using topologies with different levels of connectivity. We show that the particles tend to search a region whose size decreases as the connectivity of the population topology increases. We therefore put forward the idea that spatial convergence, and not a random behavior, is the cause of the poor performance of FIPS with a fully connected topology. The practical implications of this result are explored.