A Hybrid Evolutionary Algorithm Based on ACO and SA for Cluster Analysis

T. Niknam, J. Olamaei, B. Amiri
2008 Journal of Applied Sciences  
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
more » ... 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.
doi:10.3923/jas.2008.2695.2702 fatcat:5qvdm7b7prgkxcrxqjvpcdmaki