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Swarm Intelligence Clustering Algorithm based on Attractor

  • Qingyong Li
  • Zhiping Shi
  • Zhongzhi Shi

Abstract

Ant colonies behavior and their self-organizing capabilities have been popularly studied, and various swarm intelligence models and clustering algorithms also have been proposed. Unfortunately, the cluster number is often too high and convergence is also slow. We put forward a novel structure-attractor, which actively attracts and guides the ant’s behavior, and implement an efficient strategy to adaptively control the clustering behavior. Our experiments show that swarm intelligence clustering algorithm based on attractor (SICBA for short) greatly improves the convergence speed and clustering quality compared with LF and also has many notable virtue such as flexibility, decentralization.

Keywords

Convergence Speed Cluster Performance Swarm Intelligence Cluster Number Cluster Quality 
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/Wien 2005

Authors and Affiliations

  • Qingyong Li
    • 1
  • Zhiping Shi
    • 1
  • Zhongzhi Shi
    • 1
  1. 1.Institute of Computing TechnologyChinese Academy of SciencesBeijingChina

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