A Set-Based Particle Swarm Optimization Method

  • Christian B. Veenhuis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5199)


The representation used in Particle Swarm Optimization (PSO) is an n-dimensional vector. If you want to apply the PSO method, you have to encode your problem as fix-sized vector. But many problem domains have solutions of unknown sizes as for instance in data clustering where you often don’t know the number of clusters in advance.

In this paper a set-based PSO is proposed which replaces the position and velocity vectors by position and velocity sets realizing this way a PSO with variable length representation. All operations of the PSO update equations are redefined in an appropriate manner. Additionally, an operator reducing set bloating effects is introduced.

The presented approach is applied to well-known data clustering problems and performs better as other algorithms on them.


Particle Swarm Optimization Reduction Operator Dynamic Bayesian Network Particle Swarm Optimization Method Standard Particle Swarm Optimization 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Christian B. Veenhuis
    • 1
  1. 1.Fraunhofer IPK, Dept. Security TechnologyBerlinGermany

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