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.
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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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Xiao, W., Yang, Y., Xing, H., Meng, X. (2015). Clustering Algorithm Based on Fruit Fly Optimization. In: Ciucci, D., Wang, G., Mitra, S., Wu, WZ. (eds) Rough Sets and Knowledge Technology. RSKT 2015. Lecture Notes in Computer Science(), vol 9436. Springer, Cham. https://doi.org/10.1007/978-3-319-25754-9_36
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DOI: https://doi.org/10.1007/978-3-319-25754-9_36
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