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A hierarchical particle swarm optimizer

Authors: Stefan Janson; Martin Middendorf;

A hierarchical particle swarm optimizer

Abstract

A hierarchical version of the particle swarm optimization method called H-PSO is introduced. In H-PSO the particles are arranged in a dynamic hierarchy that is used to define a neighborhood structure. Depending on the quality of their so far best found solution the particles move up or down the hierarchy so that good particles have a higher influence on the swarm. Moreover, the hierarchy is used to define different search properties for the particles. Several variants of H-PSO are compared experimentally with variants of the standard PSO.

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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!
29
Top 10%
Top 10%
Top 10%
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