• ISSN: 2287-4844 (Print), 2287-4852 (Online)
    • Abbreviated Title: Prog. Intell. Comput. Appl.
    • Frequency: Annually
    • Editor-in-Chief: Dr. William Guo
    • Executive Editor:  Xian Zhang
    • Published by: Australasian Professional Development and Academic Services (APDAS)(registered from Feb 2013)
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    • E-mail: pica@etpub.com
PICA 2012 Vol.1(1): 16-36 ISSN: 2287-4844 (Print); 2287-4852 (Online)
doi: 10.4156/pica.vol1.issue1.2

Pollen-Based Bee Algorithm for Data Clustering – A Computational Model

1 David Bradford Jr., 2 Chih-Cheng Hung
Abstract: The emergent collective intelligence of social insect groups, including honey bees, presents an appealing model for problem solving. Our newly designed algorithm (which embeds bee, hive, environmental interactions, and season change) presents a more precise swarm analogy that allows our bee model to converge autonomously upon high-quality solutions. We develop in a novel way the natural concept of pollen depletion which, along with creating unique methods of field and honeycomb management, increases optimal solution exploitation – while at the same time dramatically reducing user-facing complexity. The experimental results presented, on pattern data clustering and image segmentation problems, show that the innovations of our proposed model increased both accuracy and reliability over that achieved by traditional bee models.

Keywords: Data Clustering Algorithm, Image Classification, Bees Algorithm

1 Center for Biometrics Research, Southern Polytechnic State University, Marietta GA, USA, dbradfor@spsu.edu
2 Professor of Computer Science at Southern Polytechnic, Center for Biometrics Research, Southern Polytechnic State University, Marietta GA, USA Member, IEEE, chung@spsu.edu

[PDF]

Cite: David Bradford Jr., Chih-Cheng Hung, "Pollen-Based Bee Algorithm for Data Clustering – A Computational Model," Progress in Intelligent Computing and Applications , vol. 1, no. 1, pp. 16-36, October 2012.

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