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
Multi-ring Particle Swarm Optimization
SBRN '08 Proceedings of the 2008 10th Brazilian Symposium on Neural Networks
Computational Intelligence: An Introduction
Computational Intelligence: An Introduction
Rapid and brief communication: Evolutionary extreme learning machine
Pattern Recognition
ICTAI '11 Proceedings of the 2011 IEEE 23rd International Conference on Tools with Artificial Intelligence
Evolutionary extreme learning machine – based on particle swarm optimization
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
An improved extreme learning machine based on particle swarm optimization
ICIC'11 Proceedings of the 7th international conference on Intelligent Computing: bio-inspired computing and applications
Neighborhood topologies in fully informed and best-of-neighborhood particle swarms
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Effect of the PSO Topologies on the Performance of the PSO-ELM
SBRN '12 Proceedings of the 2012 Brazilian Symposium on Neural Networks
Expert Systems with Applications: An International Journal
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In recent years, the Extreme Learning Machine (ELM) has been hybridized with the Particle Swarm Optimization (PSO) and such hybridization is called PSO-ELM. In most of these hybridizations, the PSO uses the Global topology. However, other topologies were designed to improve the performance of the PSO. In the literature, it is well known that the performance of the PSO depends on its topology, and there is not a best topology for all problems. Thus, in this paper, we investigate the effect of eight PSO topologies on performance of the PSO-ELM. The results showed empirically that the Global topology was more promising than all other topologies in optimizing the PSO-ELM according to the root mean squared error (RMSE) on the validation set in most of the evaluated datasets. However, no correlation was detected between this good performance on the RMSE and the testing accuracy.