Clustering Algorithm Based on Fruit Fly Optimization
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.
KeywordsSwarm intelligence Clustering analysis Fruit fly optimization Convergence
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).
- 2.Deneubourg, J.L., Goss,S., Franks, N., et al.: The dynamics of collective sorting: robot-like ant and ant-like robots. In: The First Conference on Simulation of Adaptive Behavior: From Animals to Animals, pp. 356–365 (1991)Google Scholar
- 3.Omran, M., Salman, A., Engelbrecht, A.P.: Image classification using particle swarm optimization. In: The 4th Asia-Pacific Conference on Simulated Evolution and Learning, pp. 370–374 (2002)Google Scholar
- 7.Yazdani, D., Saman, B., Sepas, A., et al.: A new algorithm based on improved artificial fish swarm algorithm for data clustering. Artif. Intell. 13(11), 170–192 (2013)Google Scholar
- 14.Li, C., Xu, S., Li, W., et al.: A novel modified fly optimization algorithm for designing the self-tuning proportional integral derivative controller. J. Convergence Inf. Technol. 16(7), 69–77 (2012)Google Scholar
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 2.5 International License (http://creativecommons.org/licenses/by-nc/2.5/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.