A New Method Based on Fuzzy C-means and Fuzzy Memetic Algorithm for Fuzzy Clustering Problem

Fuzzy clustering is an important problem which is the subject of active research in several real-world applications. Fuzzy c-means (FCM) algorithm is one of the most popular fuzzy clustering techniques because it is efficient, straightforward, and easy to implement. However, FCM is sensitive to initialization and is easily trapped in local optima. Genetic algorithms (GAs) are believed to be effective on NP-complete global optimization problems, and they can provide good near-optimal solutions in reasonable time. Memetic algorithms (MAs) were presented as evolutionary algorithms that hybridize the global optimization characteristics of GAs with local search techniques that allowed the GAs to perform a more deep exploitation of the solutions. In this paper, a hybrid fuzzy clustering method based on FCM and fuzzy memetic (FMA) is proposed which make use of the merits of both algorithms and resulting more efficient time performance. Experimental results show that our proposed method is efficient and can reveal encouraging results.

Keywords:

genetic algorithm , fuzzy clustering , fuzzy memetic algorithm , fuzzy c-means algorithm (FCM)