Subscribe Now Subscribe Today
Science Alert
FOLLOW US:     Facebook     Twitter
Curve Top
Journal of Applied Sciences
  Year: 2008 | Volume: 8 | Issue: 15 | Page No.: 2695-2702
DOI: 10.3923/jas.2008.2695.2702
Facebook Twitter Digg Reddit Linkedin StumbleUpon E-mail
A Hybrid Evolutionary Algorithm Based on ACO and SA for Cluster Analysis
T. Niknam, J. Olamaei and B. Amiri

This study presents an efficient hybrid evolutionary optimization algorithm based on combining Ant Colony Optimization (ACO) and Simulated Annealing (SA), called ACO-SA, for optimal clustering N object into K clusters. The new ACO-SA algorithm is tested on several data sets and its performance is compared with those of ACO, SA and K-means clustering. The simulation results show that the proposed evolutionary optimization algorithm is robust and suitable for data clustering.
PDF Fulltext XML References Citation Report Citation
  •    A Modified Tabu Search Approach for the Clustering Problem
  •    Optimal Computerized Model for Designing Cellular Manufacturing Systems using Neural Network
How to cite this article:

T. Niknam, J. Olamaei and B. Amiri, 2008. A Hybrid Evolutionary Algorithm Based on ACO and SA for Cluster Analysis. Journal of Applied Sciences, 8: 2695-2702.

DOI: 10.3923/jas.2008.2695.2702








Curve Bottom