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Improved Gravitation Field Algorithm and Its Application in Hierarchical Clustering

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  • Ming Zheng
  • Ying Sun
  • Gui-xia Liu
  • You Zhou
  • Chun-guang Zhou

Abstract

Background: Gravitation field algorithm (GFA) is a new optimization algorithm which is based on an imitation of natural phenomena. GFA can do well both for searching global minimum and multi-minima in computational biology. But GFA needs to be improved for increasing efficiency, and modified for applying to some discrete data problems in system biology. Method: An improved GFA called IGFA was proposed in this paper. Two parts were improved in IGFA. The first one is the rule of random division, which is a reasonable strategy and makes running time shorter. The other one is rotation factor, which can improve the accuracy of IGFA. And to apply IGFA to the hierarchical clustering, the initial part and the movement operator were modified. Results: Two kinds of experiments were used to test IGFA. And IGFA was applied to hierarchical clustering. The global minimum experiment was used with IGFA, GFA, GA (genetic algorithm) and SA (simulated annealing). Multi-minima experiment was used with IGFA and GFA. The two experiments results were compared with each other and proved the efficiency of IGFA. IGFA is better than GFA both in accuracy and running time. For the hierarchical clustering, IGFA is used to optimize the smallest distance of genes pairs, and the results were compared with GA and SA, singular-linkage clustering, UPGMA. The efficiency of IGFA is proved.

Suggested Citation

  • Ming Zheng & Ying Sun & Gui-xia Liu & You Zhou & Chun-guang Zhou, 2012. "Improved Gravitation Field Algorithm and Its Application in Hierarchical Clustering," PLOS ONE, Public Library of Science, vol. 7(11), pages 1-10, November.
    • Handle: RePEc:plo:pone00:0049039
    DOI: 10.1371/journal.pone.0049039
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    References listed on IDEAS

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    1. Diane G O Saunders & Joe Win & Liliana M Cano & Les J Szabo & Sophien Kamoun & Sylvain Raffaele, 2012. "Using Hierarchical Clustering of Secreted Protein Families to Classify and Rank Candidate Effectors of Rust Fungi," PLOS ONE, Public Library of Science, vol. 7(1), pages 1-14, January.
    2. Nazar Zaki & Salah Bouktif & Sanja Lazarova-Molnar, 2011. "A Combination of Compositional Index and Genetic Algorithm for Predicting Transmembrane Helical Segments," PLOS ONE, Public Library of Science, vol. 6(7), pages 1-8, July.
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