International Journal of Bioinformatics and Biomedical Engineering
Articles Information
International Journal of Bioinformatics and Biomedical Engineering, Vol.1, No.2, Sep. 2015, Pub. Date: Aug. 26, 2015
Recapitulation of Ant Colony and Firefly Optimization Techniques
Pages: 175-180 Views: 1434 Downloads: 433
[01] Anuradha , CSE&IT Dept., ITM University, Gurgaon, India.
[02] Kshitiz Adlakha, CSE&IT Dept., ITM University, Gurgaon, India.
This paper presents an analysis done on ACO and FFA optimization algorithms. Optimization algorithms have been used successfully to solve various problems in different areas. These algorithms are considered an important part of Swarm Intelligence, which refers to the social interaction of swarms of ants, termites, bees, termites and flock of birds. The methods used for the comparison are Max-Min Ant System, Ant Colony System, Rank-based Ant System and various hybrids/modified firefly algorithms. ACO and FFA can be modified and hybridized to solve diverse engineering problems. In most cases, the results provided by these algorithms meet the expected output.
Ant, Firefly, ACO, Swarm Intelligence, Meta-Heuristic, Pheromone
[01] C. Blum, X Li, Swarm intelligence in optimization, Swarm Intelligence: Introduction and Applications, Springer Verlag, Berlin, 2008, pp.43–86.
[02] M. Beekman, G. Sword, S.Simpson, Biological foundations of swarm intelligence, Swarm Intelligence: Introduction and Applications, Springer Verlag, Berlin, 2008, pp.3–41.
[03] Dorigo, M., Maniezzo, V., & Colorni, A. (1996). Ant System: Optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics – Part B, 26, 29–41.
[04] X.S. Yang, Firefly algorithm, Nature-Inspired Metaheuristic Algorithms 20 (2008)79–90.
[05] Goss, S., Aron, S., Deneubourg, J. L., & Pasteels, J. M. (1989). Self-organized shortcuts in the argentine ant. Naturwissenschaften, 76, 579–581.
[06] Cordon, O., Herrera, F., & Stutzle, T. (2002). A review on the ant colony optimization metaheuristic: Basis, models and new trends. Mathware and Soft Computing, 9(2–3): 141 175.
[07] Stutzle, T., & Hoos, H. H. (2000). Max–min ant system. Future Generation Computer Systems, 16(8), 889–914.
[08] Dorigo, M., & Di Caro, G. (1999). The Ant Colony optimization metaheuristic. New Ideas in Optimization (pp. 11–32).
[09] Dorigo, M., & Gambardella, L. M. (1997). Ant Colony System: A cooperating learning approach to the travelling salesman problem. IEEE Transactions on Evolutionary Computation, 1(1), 53–66.
[10] Bullnheimer, B., Hartl, R. F., & Strauss, C. (1996). A new rank-based version of the Ant System: A computational study. Central European Journal for Operations Research and Economics, 7(1), 25–38.
[11] Shmygelska, A., Hoos, H. H. (2003). An improved ant colony optimisation algorithm for the 2D HP protein folding problem. In Advances in artificial intelligence: 16th Conference of the Canadian society for computational studies of intelligence, Halifax, Canada, AI 2003 (page 993).
[12] Findova, S. (2006). 3D protein folding problem using ant algorithm. In Proceedings of BioPS international conference, Sofia, Bulgaria (pp. 19–26).
[13] Fernandes, C., Ramos, V., & Rosa, A. C. (2005). Self-regulated artificial ant colonies on digital image habitats. International Journal of Lateral Computing, 2(1), 1–8.
[14] Ramos, V. & Almeida, F. (2000). Artificial ant colonies in digital image habitats – a mass behaviour effect study on pattern recognition. In Proceedings of ANTS’2000
[15] Channa, A. H., Rajpoot, N. M., & Rajpoot, K. M. (2006). Texture segmentation using ant tree clustering. In 2006 IEEE international conference on engineering of intelligent systems (pp. 1–6).
[16] Ouadfel, S., & Batouche, M. (2002). Unsupervised image segmentation using a colony of cooperating ants. In BMCV ’02: Proceedings of the second international workshop on biologically motivated computer vision (pp. 109–116). Springer-Verlag.
[17] Fayyad, U. M., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery: An overview. In Advances in knowledge discovery and data mining (pp. 1–34). Cambridge, MA: AAAI/MIT.
[18] Edelstein, H. A. (1999). Introduction to data mining and knowledge discovery (3rd ed.). Two Crows Corporation.
[19] A. Eiben, J. Smith, Introduction to Evolutionary Computing, Springer-Verlag, Berlin, 2003.
[20] X.S. Yang, Firefly algorithm, stochastic test functions and design optimisa- tion, International Journal of Bio-Inspired Computation 2 (2) (2010) 78–84.
[21] X.S. Yang, Review of meta-heuristics and generalised evolutionary walk algorithm, International Journal of Bio-Inspired Computation 3 (2) (2011) 77–84.
[22] X.S. Yang, Metaheuristic optimization: algorithm analysis and open problems, in: P. Pardalos, S. Rebennack (Eds.), Experimental Algorithms, Lecture notes in Computer Science, vol. 6630, Springer Verlag, Berlin, 2011, pp. 21–32.
[23] X.S. Yang, Firefly algorithm, levy flights and global optimization, in: M. Bramer, R. Ellis, M. Petridis (Eds.), Research and Development in Intelligent Systems XXVI, Springer, 2010, pp. 209–218.
[24] X.S. Yang, Efficiency analysis of swarm intelligence and randomization techniques, Journal of Computational and Theoretical Nanoscience 9 (2) (2012) 189–198.
[25] J. Holland, Adaptation in Natural and Artificial Systems, 1st edition, MIT Press, Cambridge, USA, 1992.
[26] J. Kennedy, R. Eberhart, The particle swarm optimization: social adaptation in information processing, in: D. Corne, M. Dorigo, F. Glover (Eds.), New Ideas in Optimization, McGraw Hill, London, UK, 1999, pp. 379–387.
[27] I. Fister et al. in Swarm and Evolutionary Computation 13 (2013), pp. 34–46
[28] R. Mallipeddi, P. Suganthan, Q. Pan, M. Tasgetiren, Differential evolution algorithm with ensemble of parameters and mutation strategies, Applied Soft Computing 11 (2) (2011) 1679–1696
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