The week's pick
Reseach Article
Unsupervised Updation Strategies for ACO Algorithms
by
Raghavendra G S,
Prasanna Kumar N
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 30 - Number 11 |
| Year of Publication: 2011 |
| Authors: Raghavendra G S, Prasanna Kumar N |
10.5120/3694-5118
|
Raghavendra G S, Prasanna Kumar N . Unsupervised Updation Strategies for ACO Algorithms. International Journal of Computer Applications. 30, 11 ( September 2011), 37-43. DOI=10.5120/3694-5118
@article{
10.5120/3694-5118,
author = {
Raghavendra G S,
Prasanna Kumar N
},
title = { Unsupervised Updation Strategies for ACO Algorithms },
journal = {
International Journal of Computer Applications
},
issue_date = { September 2011 },
volume = { 30 },
number = { 11 },
month = { September },
year = { 2011 },
issn = { 0975-8887 },
pages = {
37-43
},
numpages = {9},
url = {
https://ijcaonline.org/archives/volume30/number11/3694-5118/
},
doi = { 10.5120/3694-5118 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:16:54.842922+05:30
%A Raghavendra G S
%A Prasanna Kumar N
%T Unsupervised Updation Strategies for ACO Algorithms
%J International Journal of Computer Applications
%@ 0975-8887
%V 30
%N 11
%P 37-43
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract
Ant Colony Optimization (ACO) algorithms belong to class of metaheuristic algorithms, where a search is made for optimized solution rather than exact solution, based on the knowledge of the problem domain. ACO algorithms are iterative in nature. As the iteration proceeds, solution converges to the optimized solution. In this paper, we propose new updation mechanism based on clustering techniques, an unsupervised learning mechanism aimed at exploring the nearby solutions region. We also report in detail the impact on performance due to integration of cluster and ACO.
References
- Dorigo,M., Maniezzo, V., and Colorni, A.(1996) Ant System: Optimization by a colony of cooperating agents, IEEE Transaction on Systems, Man and Cybernetics, Vol 26,No.1,pp.29-41.
- Bullnheimer, Hartl, R. F., and Strauss, C.(1999) A new rank based version of the Ant System: A computational study, Central European Journal for Operation Research and Economics, Vol 7,No.1,pp.25-38.
- Blum, C., Roli, A., and Dorigo, M.(2004) HC-ACO: The Hypercube framework for Ant Colony Optimization, IEEE Transaction on Systems, Man and Cybernetics, Vol 34,No.2,pp.1161-1172.
- Gambardella, L.M., and Dorigo, M.(1995) Ant-Q: A reinforcement learning approach to the traveling salesman problem. In Proceedings of 12th International Conference on Machine Learning.
- Gambardella, L.M., and Dorigo. M.(1997) Solving symmetric and asymmetric TSP by ant colonies. In Proceedings of IEEE International Conference on Evolutionary Computation. Porto,Spain.
- Stutzle, T., and Hoos, H.H.(2000) MAX - MIN Ant System, Future Generation Computer System, Vol 16,No 8,pp.889-914.
- Hong-hao, Z., and Fan-lun, X.(2006) A New Approach of Ant Colony Optimization and its Proof of Convergence. In Proceedings of 6th World Congress on Intelligent Control and Automation.
- Jun, S., Sheng-Wu and Fu-Ming, G.(2004) A New Pheromone Updating Strategy in Ant Colony Optimization. In Proceedings of 3rd International Conference on Machine Learning and Cybernetics.
- Lijie, L., Shangyou, J., and Ying, Z.(2008) Improved Ant Colony Optimization for the Traveling Salesman Problem. In Proceedings of International Conference on Intelligent Computation Technology and Automation.
- Masaya, Y., Masahiro, F., and Hidekazu, T.(2008) A New Pheromone Control Algorithm of Ant Colony Optimization. In Proceedings of International Conference on Smart Manufacturing Application.
- Naimi, H.M., and Taherinejad, N.(2009) New Robust and efficient ant colony algorithms: Using new interpretation of local updating process, Expert System with Applications, Vol 36,No.1,pp.481-488.
- Stutzle, T., and Dorigo, M.(2002) A Short Convergence Proof for a Class of Ant Colony Optimization, IEEE Transaction on Evolutionary Computation, Vol 20,No.3,pp.1-9.
- Raghavendra, G. S., and Prasanna K.N.(2008) Relative Reward Pheromone Update Strategy for ACO Algorithms. In Proceedings of MSAST2008, IMBIC,Kolkata,India.
- Blum, C.(2007). Ant Colony Optimization: Introduction and Hybridization. In Proceedings of 7th International Conference on Hybrid Intelligent Systems.
- MacQueen J (1967) Some methods for classification and analysis of multi-variate observations. In Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, pp.281-297.
- Prasanna, K., and Raghavendra, G.S.(2011) On the Evaporation Mechanism in the Ant Colony Optimization Algorithms, Annales of Computer Science Series, Vol 9,No.1,pp.51-56.
- Prasanna, K., and Raghavendra, G.S.(2011) A note on the Parameter of Evaporation Mechanism in the Ant Colony Optimization Algorithms, International Mathematical Forum, Vol 6,No.34,pp.1655-1659.
- Wang, W., Yang, J., Muntz, R (1997) STING: A Statistical information grid approach to spatial data mining. In Proceedings of International Conference on Very Large Databases, pp.186-195.
- Jones T, Forrest S (1995) Fitness distance correlation as a measure of problem difficulty for genetic algorithms. In Proceedings of the 6th International Conference on Genetic Algorithms, pp.184-192.
- Rui X, Donald W (2005) Survey of Clustering Algorithms. IEEE Transaction On Neural Networks, Vol.16,No 3,pp.645-678.
- Martin, E., Kriegel, H., Sander, J. and Xiaowei X (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96), pp.226-231.
Index Terms
Computer Science
Information Sciences
Keywords