Fuzzy tabu search method for the clustering problem

In this paper, the clustering problem (CP) of clustering m objects into c clusters is considered. The objects are represented by points in n-dimensional Euclidean space, and the objective is to classify these m points into c groups so that the distance between points within a cluster and its center is minimized. The problem is an optimization problem that has many local minima. In this paper, we develop a novel fuzzy tabu search (FTS) method for solving this problem. Benefits of the proposed algorithm are illustrated by the numerical results. The obtained results are compared with the standard tabu search method.