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Intrusive tumor growth inspired optimization algorithm for data clustering

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Abstract

Inspired by the invasive tumor growth mechanism, this paper proposes a new meta-heuristic algorithm. A population of tumor cells can be divided into three subpopulations as proliferative cells, quiescent cells, and dying cells according to the nutrient concentration they get. Different cells have different behaviors and interactions among them for competition. In the tumor growing process, an invasive cell is born around a proliferative cell for the higher nutrient concentration and a necrotic cell occurs around a dying cell for the lower nutrient concentration, which presents the balance between life and death. To evaluate the performance of the intrusive tumor growth optimization algorithm (ITGO), we compared it to the many well-known heuristic algorithms by the Wilcoxon’s signed-rank test with Bonferroni–Holm correction method and the Friedman’s test. At the end, it is applied to solve the data clustering problem, which is a NP-hard problem. The experimental results show that the proposed ITGO algorithm outperforms other traditional heuristic algorithms for several benchmark datasets.

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Acknowledgments

This work is supported by the National Natural Science Foundation Project (No. 61070092/F020504); the building of strong Guangdong Province for Chinese Medicine Scientific Research (20141165); the Humanities and social science fund project for Guangdong Pharmaceutical University (RWSK201409).

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Authors and Affiliations

  1. School of Computer Science and Engineering, South China University of Technology, Guangzhou, 510006, People’s Republic of China

    Deyu Tang, Shoubin Dong & Lifang He

  2. School of Medical Information and Engineering, Guangdong Pharmaceutical University, Guangzhou, 510006, People’s Republic of China

    Deyu Tang

  3. Department of Mathematics and Statistics, Georgia State University, Atlanta, GA, USA

    Yi Jiang

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  1. Deyu Tang
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  2. Shoubin Dong
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Correspondence to Shoubin Dong.

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Tang, D., Dong, S., He, L. et al. Intrusive tumor growth inspired optimization algorithm for data clustering. Neural Comput & Applic 27, 349–374 (2016). https://doi.org/10.1007/s00521-015-1849-4

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