Intrusive tumor growth inspired optimization algorithm for data clustering

De-Yu Tang, Shoubin Dong, Lifang He, Yi Jiang

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.