Open Access
| Issue |
ITM Web Conf.
Volume 7, 2016
3rd Annual International Conference on Information Technology and Applications (ITA 2016)
|
|
|---|---|---|
| Article Number | 05011 | |
| Number of page(s) | 7 | |
| Section | Session 5: Algorithms and Simulation | |
| DOI | https://doi.org/10.1051/itmconf/20160705011 | |
| Published online | 21 November 2016 | |
- M. Dorigo, “Optimization learning and natural algorithms”, PhD Thesis, Dipartimento Elettronica, Politecnico di Milano, Italy, 1992. [Google Scholar]
- M. Dorigo, L. M. Gambardella, “Ant colony system: a cooperative learning approach to the traveling salesman problem”, IEEE Transactions on Evolutionary Computation, Vol. 1, No. 1, pp.53–66, 1997. [Google Scholar]
- M. Dorigo, L. M. Gambardella, “Ant colonies for the traveling salesman problem”, BioSystems, Vol. 43, No. 1, 1997, pp. 73–81. [CrossRef] [PubMed] [Google Scholar]
- L. M. Gambardella, M. Dorigo, “Solving symmetric and asymmetric TSPs by ant colonies”, Proceedings of the IEEE International Conference on Evolutionary Computation, IEEE Press, Piscataway, pp.622–627, 1996. [CrossRef] [Google Scholar]
- Luca M. Gambardella, M. Dorigo. “Ant-Q: A Reinforcement Learning approach to the traveling salesman problem”, Proceedings of ML-95, Twelfth Intern. Conf. on Machine Learning, Morgan Kaufmann, 1995, 252–260. [Google Scholar]
- Marco Dorigo, Luca Maria Gambardella. “A STUDY OF SOME PROPERTIES OF ANT-Q”, Proceedings of PPSN IV–Fourth International Conference on Parallel Problem Solving From Nature, H.–M. Voigt, Ebeling, W., I. Rechenberg and H.–S. Schwefel (Eds.), Springer-Verlag, Berlin, 656–665. [Google Scholar]
- B. Bullnheimer, R.F. Hartl, and C. Strauss. A new rank-based version of the ant system: A computational study[J].Central Europeon Journal for Operations Research and Economics,7(1):25–38,1999. [Google Scholar]
- T. Stützle and H. Hoos. The MAX-MIN ant system and local search for 9. the traveling salesman problem[C]. In T. Baeck, Z. Michalewicz, and X. Yao, editors Proceedings of IEEE-ICEC-EPS’ 97, IEEE International 10. Conference on Evolutionary Computation and Evolutionary Programming Conference, pages 309–314. IEEE Press, 1997. [Google Scholar]
- T. Stützle and H. Hoos. Improvements on the ant system: Introducing MAX-MIN ant system[C]. In Proceedings of the International Conference on Artificial Neural Networks and Genetic Algorithms, pages 245–249. Springer Verlag, Wien, 1997. [Google Scholar]
- O. Cordón, I. Fernández de Viana, F. Herrera, and L. Moreno. A New ACO model intergrating evolutionary computation concepts: The best-worst ant system[C]. Proc of the 2nd International Workshop on Ant Algorithms, 2000: 22–29. [Google Scholar]
- C. Immaculate Mary, Dr. S.V. Kasmir Raja, Dean. Improved Fuzzy C-Means Clusters With Ant Colony Optimization. International Journal of Computer Science & Emerging Technologies (E-ISSN: 2044-6004) 1Volume 1, Issue 4, December 2010 [Google Scholar]
- Marco Dorigo, Christian Blum. Ant colony optimization theory: A survey. Theoretical Computer Science 344 (2005) 243–278 [Google Scholar]
- Han-Chen Huang. “The Application of Ant Colony Optimization Algorithm In Tour Route Planning”, Journal of Theoretical and Applied Information Technology, 2013 [Google Scholar]
- Jian-Feng Yang. “The Ant Colony Algorithm And Its Application Research”. CNKI, 2007:23–26 [Google Scholar]
- Mr. Pankaj K. Bharne, Mr. V. S. Gulhane, Miss. Shweta K. Yewale: Data Clustering Algorithms Based On Swarm Intelligence”. IEEE. 2011 [Google Scholar]
- http://archive.ics.uci.edu/ml [Google Scholar]
- Sara Saatchi, Chih Cheng Hung. “Hybridization of the Ant Colony Optimization with the K-Means Algorithm for Clustering”. SCIA 2005. [Google Scholar]
- P.S. Shelokar, V.K. Jayaraman, B.D. Kulkarni*. “An ant colony approach for clustering”. Analytica Chimica Acta 2004. [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.