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
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
Accelerating the performance of particle swarm optimization for embedded applications
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Design of artificial neural networks using a modified particle swarm optimization algorithm
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
A modular and efficient hardware architecture for particle swarm optimization algorithm
Microprocessors & Microsystems
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This paper investigates the use of Random Dynamic Neighborhoods in Particle Swarm Optimization (PSO) for the purposeof training fixed-architecture neural networks to classify a real-world data set of seismological data.Instead of the ring or fully-connected neighborhoods that are typically used with PSOs, or even more complex graph structures, this work uses directed graphs that are randomly generated using size and uniform out-degree as parameters. Furthermore, the graphs are subjected to dynamism during the course of a run, thereby allowing for varying information exchange patterns. Neighborhood re-structuring is applied with a linearly decreasing probability at each iteration. Several experimental configurations are tested on a training portion of the data set, and are ranked according to their abilities to generalize over the entire set. Comparisons are performed with standard PSOs as well as several static non-random neighborhoods.