Evaluation of optimization metaheuristics in clustering

We have evaluated five metaheuristics of combinatorial optimization applied in clustering by partitions: simulated annealing, tabu search, genetic algorithm, ant colonies and particle swarms, using data tables generated randomly according to some defined parameters. Those techniques were compared to classical methods (k-means and Ward´s agglomerative clustering). Sixteen tables were generated (four controlled factors, with two levels each) with normally distributed variables and, for each one, the experiment was repeated 100 times in a multistart procedure. The within-class inertia was used as the criterion to compare the classifications obtained. Best results were obtained for ant colonies, simulated annealing and the genetic algorithm.