Optimization of fuzzy C-means based on OBL-genetic algorithm

In previous studies, many scholars use the global search ability of Genetic Algorithm (GA) to optimize the Fuzzy C-means (FCM) clustering algorithm to obtain better clustering center. But the prematurity problem of GA itself has bad effects on the whole clustering. Therefore, in order to optimize the traditional GA-FCM algorithm's clustering effect, in this paper, we introduce the Opposition-based learning mechanism into GA, to construct an OBL-Genetic Algorithm (OBL-GA). The improved algorithm forms the next generation of evolutionary population by selecting the superior individuals in the collection of the sub generation and reverse sub generation, to increase the population diversity, and final to overcome the prematurity problem of GA. Then applying the improved algorithm to FCM, at the last of the paper, we carried out a series of experiments to verify the effectiveness of it.