TY - GEN
T1 - Ant colony optimization with dual pheromone tables for clustering
AU - Tsai, Chun Wei
AU - Hu, Kai Cheng
AU - Chiang, Ming Chao
AU - Yang, Chu-Sing
PY - 2011/9/27
Y1 - 2011/9/27
N2 - This paper presents a novel pheromone update strategy for improving the clustering results of ant colony optimization (ACO). The proposed algorithm is motivated by the observation that most of the ACOs only keep track of the promising foraging information, which has the potential to lead to better solutions than all the other search directions in the pheromone table. This eventually makes the search converge to particular search directions in later iterations because the pheromone values on good routing paths will be reinforced. As such, the breadth of search (diversity) will be reduced, thus limiting the clustering results of ACO. The proposed algorithm adds a second pheromone table to ACO for recording the unpromising foraging information that is worse than all the other search directions and using a novel construction method to explore the new search directions. In other words, by leveraging the strengths of diversification and intensification, the proposed algorithm can find better solutions than traditional ACO. To evaluate the performance of the proposed algorithm, we use it to solve the data clustering problem. Our experimental results indicate that the proposed algorithm can significantly improve the quality of ant colony optimization.
AB - This paper presents a novel pheromone update strategy for improving the clustering results of ant colony optimization (ACO). The proposed algorithm is motivated by the observation that most of the ACOs only keep track of the promising foraging information, which has the potential to lead to better solutions than all the other search directions in the pheromone table. This eventually makes the search converge to particular search directions in later iterations because the pheromone values on good routing paths will be reinforced. As such, the breadth of search (diversity) will be reduced, thus limiting the clustering results of ACO. The proposed algorithm adds a second pheromone table to ACO for recording the unpromising foraging information that is worse than all the other search directions and using a novel construction method to explore the new search directions. In other words, by leveraging the strengths of diversification and intensification, the proposed algorithm can find better solutions than traditional ACO. To evaluate the performance of the proposed algorithm, we use it to solve the data clustering problem. Our experimental results indicate that the proposed algorithm can significantly improve the quality of ant colony optimization.
UR - http://www.scopus.com/inward/record.url?scp=80053057167&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=80053057167&partnerID=8YFLogxK
U2 - 10.1109/FUZZY.2011.6007567
DO - 10.1109/FUZZY.2011.6007567
M3 - Conference contribution
AN - SCOPUS:80053057167
SN - 9781424473175
T3 - IEEE International Conference on Fuzzy Systems
SP - 2916
EP - 2921
BT - FUZZ 2011 - 2011 IEEE International Conference on Fuzzy Systems - Proceedings
T2 - 2011 IEEE International Conference on Fuzzy Systems, FUZZ 2011
Y2 - 27 June 2011 through 30 June 2011
ER -