Abstract
A social insect’s techniques become more focus by researchers because of its nature behavior processing and by training neural networks through agents. Chief among them are Swarm Intelligence (SI), Ant Colony Optimization (ACO), and recently Artificial Bee Colony algorithm, which produced easy way for solving combinatorial problems and for training NNs. These social based techniques mostly used for finding optimal weight values in NNs learning. Usually, NNs trained by a standard and well known algorithm called Backpropagation (BP) have difficulties such as trapping in local minima, slow convergence or might fail sometimes. For recovering the above cracks the population or social insects based algorithms used for training NNs for minimizing network output error. Here, the hybrid of nature behavior agents’ ant and bees combine’s techniques used for training ANNs. The simulation result of a hybrid algorithm compared with, ABC and BP training algorithms. From the experimental results, the proposed Hybrid Ant Bee Colony (HABC) algorithm did improve the classification accuracy for prediction of a volcano time-series data.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
References
Romano, Michele, A.U., et al.: Artificial neural network for tsunami forecasting. Journal of Asian Earth Sciences 36, 29–37 (2009)
Ghazali, R., Hussain, A.J., Liatsis, P.: Dynamic Ridge Polynomial Neural Network: Forecasting the univariate non-stationary and stationary trading signals. Expert Systems with Applications 38(4), 3765–3776 (2011)
Rumelhart, D.E., McClelland, J.L.: And the PDP Research Group,Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol. 1, 2. MIT Press, Cambridge (1986)
Osamu, F.: Statistical estimation of the number of hidden units for feedforward neural networks. Neural Networks 11(5), 851–859 (1998)
Shokri, A., Hatami, T., et al.: Near critical carbon dioxide extraction of Anise (Pimpinella Anisum L.) seed: Mathematical and artificial neural network modeling. The Journal of Supercritical Fluids 58(1), 49–57 (2011)
Thwin, M.M.T., Quah, T.-S.: Application of neural networks for software quality prediction using object-oriented metrics. Journal of Systems and Software 76(2), 147–156 (2005)
Taylor, S.R., Denny, M.D., Vergino, E.S., Glaser, R.E.: Regional discrimination between NTS explosions and earthquakes. B. Seismol. Soc. Am. 79, 1142–1176 (1989)
Adeli, H., Panakkat, A.: A probabilistic neural network for earthquake magnitude prediction. Earthquake Engineering 22, 1018–1024 (2009) ISBN- 0893-6080
Connor, J., Atlas, L.: Recurrent Neural Networks andTime Series Prediction. In: IEEE International Joint Conference on Neural Networks, New York, USA, pp. I 301–I 306.
Scarpetta, S., Giudicepietro, F., Ezin, E. C., Petrosino, S., Del Pezzo, E., Martini, M., and Marinaro, M.: Automatic Classification of seismic signals at Mt. Vesuvius Volcano, Italy using Neural Networks. B. Seismol. Soc. Am., 95, 185–196, (2005)
Hornik, K., Stinchcombe, M., White, H.: Multilayer feedforward networks are universal approximators. Neural Networks 2(5), 359–366 (1989)
Cass, R., Radl, B.: Adaptive process optimization using functional-link networks and evolutionary algorithm. Control Engineering Practice 4(11), 1579–1584 (1996)
Gail, A. C.: Network models for pattern recognition and associative memory. Neural Networks 2(4), 243–257 (1989)
Tyan, C.-Y., Wang, P.P.: An application on intelligent control using neural network and fuzzy logic. Neurocomputing 12(4), 345–363 (1996)
Shah, H., Ghazali, R., Nawi, N.M.: Using Artificial Bee Colony Algorithm for MLP Training on Earthquake Time Series Data Prediction. Journal of Computing 3(6), 135–142 (2011)
Rosenblatt, F.: A Probabilistic Model for Information Storage and Organization in the Brain, vol. 65, pp. 386–408. Cornell Aeronautical Laboratory (1958)
Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press, NY (1999)
Yao, X.: Evolutionary artificial neural networks. International Journal of Neural Systems 4(3), 203–222 (1993)
Ilonen, J., Kamarainen, J.-K., et al.: Evolution Training Algorithm for Feed-Forward Neural Networks. Neural Processing Letters 17(1), 93–105 (2003)
Mendes, R., Cortez, P., Rocha, M., Neves, J.: Particle swarm for feedforward neural network training. In: Proceedings of the International Joint Conference on on Neural Networks, vol. 2, pp. 1895–1899 (2002)
Dorigo, M., Di Caro, G., Corne, D., Dorigo, M., Glover, F.: The ant colony optimization meta-heuristic. New Ideas in Optimization, 11–32 (1999)
Ozturk, C., Karaboga, D.: Hybrid Artificial Bee Colony algorithm for neural network training. In: 2011 IEEE Congress on Evolutionary Computation, CEC 2011 (2011)
Karaboga, D., Akay, B., Ozturk, C.: Artificial Bee Colony (ABC) Optimization Algorithm for Training Feed-Forward Neural Networks. In: Torra, V., Narukawa, Y., Yoshida, Y. (eds.) MDAI 2007. LNCS (LNAI), vol. 4617, pp. 318–329. Springer, Heidelberg (2007)
Gori, M., Tesi, A.: On the problem of local minima in back-propagation. IEEE Trans. Pattern Anal. Mach. Intell. 14(1), 76–86 (1992)
Dorigo, M., Stützle, T.: Ant Colony Optimization. MIT Press (2004)
Schneider, S.H., Easterling, W.E., Mearns, L.O.: Adaptation: Sensitivity to natural variability, agent assumptions and dynamic climate changes. Climatic Change 45, 203–221 (2000)
Tiira, T.: Detecting tele-seismic events using artificial neural networks. Comput. Geosci. 25, 929–939 (1999)
Chau, K.W.: Particle swarm optimization training algorithm for A NNs in stage prediction of Shing Mun River. Journal of Hydrology, 363–367 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Shah, H., Ghazali, R., Nawi, N.M. (2012). Hybrid Ant Bee Colony Algorithm for Volcano Temperature Prediction. In: Chowdhry, B.S., Shaikh, F.K., Hussain, D.M.A., Uqaili, M.A. (eds) Emerging Trends and Applications in Information Communication Technologies. IMTIC 2012. Communications in Computer and Information Science, vol 281. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28962-0_43
Download citation
DOI: https://doi.org/10.1007/978-3-642-28962-0_43
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28961-3
Online ISBN: 978-3-642-28962-0
eBook Packages: Computer ScienceComputer Science (R0)