Some Metaheuristic Approaches for the Clustering Problem with an Application to Failure Detection

Abstract

Clustering is a relevant problem that takes place in many practical environments. This paper presents some meta-heuristic approaches as an alternative to the traditional clustering techniques, like K-means or C-means. They are based on some metaheuristic optimization algorithms as tabu search, simulated annealing, genetic algorithms and ant colony. The developed techniques have the advantage that they could escape more efficiently from local minima. Additionally, an application on failure detection in a hydraulic system was developed and the obtained results are competitive with some well known techniques