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.
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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
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DOI: https://doi.org/10.1007/978-3-642-28962-0_43
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