A Hybrid Ant Colony Optimization Algorithm for Software Project Scheduling

International Journal of Computer Science and Engineering
© 2016 by SSRG - IJCSE Journal
Volume 3 Issue 3
Year of Publication : 2016
Authors : S.Jagadeesan, S.Gayathri

How to Cite?

S.Jagadeesan, S.Gayathri, "A Hybrid Ant Colony Optimization Algorithm for Software Project Scheduling," SSRG International Journal of Computer Science and Engineering , vol. 3,  no. 3, pp. 27-37, 2016. Crossref, https://doi.org/10.14445/23488387/IJCSE-V3I3P106


The extraction of comprehensible knowledge is one of the major challenges in many domains. In this concept, an ant programming (AP) framework, which is capable of mining classification rules easily comprehensible by humans, and, therefore, capable of supporting expert-domain decisions, is presented. The algorithm proposed, called grammar based ant programming (GBAP), is the first AP algorithm developed for the extraction of classification rules, and it is guided by a context-free grammar that ensures the creation of new valid individuals. To compute the transition probability of each available movement, this new model introduces the use of two complementary heuristic functions, instead of just one, as typical ant-based algorithms do. The selection of a consequent for each rule mined and the selection of the rules that make up the classifier are based on the use of a niching approach. The performance of GBAP is compared against other classification techniques on 18 varied data sets. Experimental results show that our approach produces comprehensible rules and competitive or better accuracy values than those achieved by the other classification algorithms compared with it.


Ant Colony Optimization (ACO), Ant Programming (AP), classification, Data Mining (DM), Grammar-Based Automatic Programming (GBAP).


[1] J. Han and M. Kamber, Data Mining: Concepts and Techniques. San Mateo, CA: Morgan Kaufman, 2006.
[2] S. B. Kotsiantis, I. D. Zaharakis, and P. E. Pintelas, ―Machine learning: A review of classification and combining techniques, Artif. Intell. Rev., vol. 26, no. 3, pp. 159–190, Nov. 2006.
[3] H.-J. Huang and C.-N. Hsu, ―Bayesian classification for data from the same unknown class, IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 32, no. 2, pp. 137–145, Apr. 2002.
[4] T.-M. Huang, V. Kecman, and I. Kopriva, ―Support vector machines in classification and regression–An introduction, in Kernel Based Al-gorithms for Mining Huge Data Sets: Supervised, Semi-supervised, and Unsupervised Learning (Studies in Computational Intelligence). New York: Springer- Verlag, 2006.
[5] S. Haykin, Neural Networks and Learning Machines, 3rd ed. Upper Saddle River, NJ: Pearson, 2009.
[6] S. U. Guan and F. Zhu, ―An incremental approach to geneticalgorithms- based classification, IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 35, no. 2, pp. 227–239, Apr. 2005.
[7] K. C. Tan, Q. Yu, C. M. Heng, and T. H. Lee. (2003, Feb.). Evolutionary computing for knowledge discovery in medical diagnosis. Artif. Intell. Med. [Online]. 27( 2), pp. 129–154. Available: http://www.sciencedirect.com/science/article/B6T4K- 47RRWS9-2/2/ 5c8dfaf6e49d194b0c8ed6e2fd1b5117
[8] M. Dorigo and T. Stützle, The Ant Colony Optimization Metaheuristic: Algorithms, Applications and Advances, F. Glover and G. Kochenberger, Eds. Norwell, MA: Kluwer, 2002, ser. International Series in Operations Research and Management Science.
[9] M. Dorigo and T. Stützle, The ant colony optimization metaheuristic: Algorithms, applications and advances, IRIDIA, Université Libre de Bruxelles, Brussels, Belgium, Tech. Rep. TR/IRIDIA/2000-32. [Online]. Available: ftp://iridia.ulb.ac.be/pub/mdorigo/tec.reps/TR.11- MetaHandBook.pdf
[10] R. Parpinelli, A. A. Freitas, and H. S. Lopes, ―Data mining with an ant colony optimization algorithm, IEEE Trans. Evol. Comput., vol. 6, no. 4, pp. 321–332, Aug. 2002.
[11] J. R. Koza, Genetic Programming: On the Programming of Computers by Means of Natural Selection. Cambridge, MA: MIT Press, 1992.
[12] O. Roux and C. Fonlupt, ―Ant programming: Or how to use ants for automatic programming, in Proc. ANTS, M. Dorigo and E. Al, Eds., 2000, pp. 121–129.
[13] P. Espejo, S. Ventura, and F. Herrera, ―A survey on the application of genetic programming to classification, IEEE Trans. Syst., Man, Cybern. C, Appl. Rev., vol. 40, no. 2, pp. 121–144, Mar. 2010.
[14] J. Fürnkranz. (1999, Jan.). Separate-and-conquer rule learning. Artif. Intell. Rev. [Online]. 13(1), pp. 3–54. Available: http://portal.acm.org/ citation.cfm?id=309283.309291
[15] E. Bonabeu, T. Eric, and M. Dorigo, Swarm Intelligence: From Natural to Artificial Systems. New York: Oxford Univ. Press, 1999.