Clustering problems appear in a wide range of unsupervised classification applications such as pattern recognition, vector quantization, data mining and knowledge discovery. The k-means algorithm is one of the most widely used clustering techniques. Unfortunately, k-means is extremely sensitive to the initial choice of centers and a poor choice of centers may lead to a local optimum that is quite inferior to the global optimum. This paper presents an efficient hybrid evolutionary optimization

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... gorithm based on combining Ant Colony Optimization (ACO) and Simulated Annealing (SA), called ACO-SA, for cluster analysis. The performance is evaluated through several benchmark data sets. The simulation results show that the proposed algorithm outperforms the previous approaches such as SA, ACO and k-means for partitional clustering problem.