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Clustering Algorithm Based on Fruit Fly Optimization

  • Wenchao Xiao
  • Yan YangEmail author
  • Huanlai Xing
  • Xiaolong Meng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9436)

Abstract

The swarm intelligence optimization algorithms have been widely applied in the fields of clustering analysis, such as ant colony algorithm, artificial immune algorithm and so on. Inspired by the idea of fruit fly optimization algorithms, this paper presents Fruit Fly Optimization Clustering Algorithm (FOCA) based on fruit fly optimization. The algorithm extends the space which fruit fly from two-dimension to three, in order to find the global optimum in each iteration. Besides, for the purpose of getting the optimize clusters centers, each fruit fly flies step by step, and every flight is a stochastic search in its own region. Compared with the other clustering algorithms of swarm intelligence, the proposed algorithm is simpler and with fewer parameters. The experimental results demonstrate that our algorithm outperforms some of state-of-the-art algorithms regarding to the accuracy and convergence time.

Keywords

Swarm intelligence Clustering analysis Fruit fly optimization Convergence 

Notes

Acknowledgements

This work is supported by the National Science Foundation of China (Nos. 61170111, 61134002 and 61401374) and the Fundamental Research Funds for the Central Universities (No. 2682014RC23).

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© Springer International Publishing Switzerland 2015

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Authors and Affiliations

  • Wenchao Xiao
    • 1
  • Yan Yang
    • 1
    Email author
  • Huanlai Xing
    • 1
  • Xiaolong Meng
    • 1
  1. 1.School of Information Science and TechnologySouthwest Jiaotong UniversityChengduPeople’s Republic of China

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