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Honey Bees Inspired Learning Algorithm: Nature Intelligence Can Predict Natural Disaster

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Recent Advances on Soft Computing and Data Mining

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 287))

Abstract

Artificial bee colony (ABC) algorithm which used the honey bee intelligence behaviors, is a new learning technique comparatively attractive for solving optimization problems. Artificial Neural Network (ANN) trained with the ABC algorithm normally has poor exploration and exploitation processes due to the random and similar strategies for finding best position of foods. Global artificial bee colony (Global ABC) and Guided artificial bee colony (Guided ABC) algorithms used to produce enough exploitation and exploration strategies respectively. Here, a hybrid of Global ABC and Guided ABC is proposed called Global Guided ABC (GG-ABC) algorithm, for getting balance and robust exploitation and exploration process. The experimental result shows that the GG-ABC performed better than other algorithms for prediction of earthquake hazards.

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References

  1. Alves, E.I.: Earthquake Forecasting Using Neural Networks: Results and Future Work. Nonlinear Dynamics 44(1-4), 341–349 (2006)

    Article  MATH  Google Scholar 

  2. Shah, H., et al.: Global Artificial Bee Colony-Levenberq-Marquardt (GABC-LM) Algorithm for Classification. International Journal of Applied Evolutionary Computation (IJAEC) 4(3), 58–74 (2013)

    Article  Google Scholar 

  3. Zette, R. (ed.): Ifrcrcs, World Disaster Report, in Focus on forced migration and displacement. International Federation of Red Cross and Red Crescent Societies:17, Chemin des Crêts, P.O.Box 372 CH-1211 Geneva 19, Switzerland, p. 310 (2012)

    Google Scholar 

  4. Adeli, H., Panakkat, A.: A probabilistic neural network for earthquake magnitude prediction. Neural Networks 22(7), 1018–1024 (2009)

    Article  Google Scholar 

  5. Kasabov, N.K.: Functionally reconfigurable general purpose parallel machines and some image processing and pattern recognition applications. Pattern Recognition Letters 3(3), 215–223 (1985)

    Article  Google Scholar 

  6. 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)

    Chapter  Google Scholar 

  7. Zhu, G., Kwong, S.: Gbest-guided artificial bee colony algorithm for numerical function optimization. Applied Mathematics and Computation 217(7), 3166–3173 (2010)

    Article  MATH  MathSciNet  Google Scholar 

  8. Shah, H., Ghazali, R., Nawi, N.M.: Global Artificial Bee Colony Algorithm for Boolean Function Classification. In: Selamat, A., Nguyen, N.T., Haron, H. (eds.) ACIIDS 2013, Part I. LNCS, vol. 7802, pp. 12–20. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  9. Adil, B., Lale, Ö., Pınar, T. (eds.): Artificial Bee Colony Algorithm and Its Application to Generalized Assignment Problem, Turkey (2007)

    Google Scholar 

  10. Peng, G., Wenming, C., Jian, L.: Global artificial bee colony search algorithm for numerical function optimization. In: 2011 Seventh International Conference on Natural Computation (ICNC) (2011)

    Google Scholar 

  11. Tuba, M., Bacanin, N., Stanarevic, N.: Guided artificial bee colony algorithm. In: Proceedings of the 5th European Conference on European Computing Conference, pp. 398–403. World Scientific and Engineering Academy and Society (WSEAS), Paris (2011)

    Google Scholar 

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Correspondence to Habib Shah .

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Shah, H., Ghazali, R., Mohmad Hassim, Y.M. (2014). Honey Bees Inspired Learning Algorithm: Nature Intelligence Can Predict Natural Disaster. In: Herawan, T., Ghazali, R., Deris, M. (eds) Recent Advances on Soft Computing and Data Mining. Advances in Intelligent Systems and Computing, vol 287. Springer, Cham. https://doi.org/10.1007/978-3-319-07692-8_21

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  • DOI: https://doi.org/10.1007/978-3-319-07692-8_21

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-07691-1

  • Online ISBN: 978-3-319-07692-8

  • eBook Packages: EngineeringEngineering (R0)

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