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Effect of the PSO Topologies on the Performance of the PSO-ELM

Authors: Elliackin M. N. Figueiredo; Teresa Bernarda Ludermir;

Effect of the PSO Topologies on the Performance of the PSO-ELM

Abstract

In recent years, the Extreme Learning Machine (ELM) has been hybridized with the Particle Swarm Optimization (PSO). This hybridization is named 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. The performance of PSO depends on its topology, and there is not a best topology for all problems. Thus, in this paper, we investigate the effect of the PSO topology on performance of the PSO-ELM. In our study, we investigate five topologies: Global, Local, Von Neumann, Wheel and Four Clusters. The results showed that the Global topology can be more promising than all other topologies.

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Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
17
Top 10%
Top 10%
Average
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