Skip to main content
SpringerLink
Log in

An Improved Gbest Guided Artificial Bee Colony (IGGABC) Algorithm for Classification and Prediction Tasks

  • Conference paper
Neural Information Processing (ICONIP 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8834))

Included in the following conference series:

Abstract

Artificial Neural Networks (ANN) performance depends on network topology, activation function, behaviors of data, suitable synapse’s values and learning algorithms. Many existing works used different learning algorithms to train ANN for getting high performance. Artificial Bee Colony (ABC) algorithm is one of the latest successfully Swarm Intelligence based technique for training Multilayer Perceptron (MLP). Normally Gbest Guided Artificial Bee Colony (GGABC) algorithm has strong exploitation process for solving mathematical problems, however the poor exploration creates problems like slow convergence and trapping in local minima. In this paper, the Improved Gbest Guided Artificial Bee Colony (IGGABC) algorithm is proposed for finding global optima. The proposed IGGABC algorithm has strong exploitation and exploration processes. The experimental results show that IGGABC algorithm performs better than that standard GGABC, BP and ABC algorithms for Boolean data classification and time-series prediction tasks.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

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

    Article  Google Scholar 

  2. 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)

    Article  Google Scholar 

  3. Karaboga, D.: An Idea based on Honey Bee Swarm for Numerical Optimization. Tech. Rep. TR06, Erciyes University, Engineering Faculty, Computer Engineering Departmen, pp. 1–10 (2005)

    Google Scholar 

  4. Romano, M., et al.: Artificial neural network for tsunami forecasting. Journal of Asian Earth Sciences 36(1), 29–37 (2009)

    Article  Google Scholar 

  5. Jun Ying, C., Zheng, Q., Ji, J.: A PSO-based subtractive clustering technique for designing RBF neural networks. In: IEEE Congress on Evolutionary Computation, CEC 2008 (IEEE World Congress on Computational Intelligence) (2008)

    Google Scholar 

  6. 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 (LNAI), vol. 7802, pp. 12–20. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  7. Zhang, G., et al.: Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis. European Journal of Operational Research 116(1), 16–32 (1999)

    Article  MATH  Google Scholar 

  8. Sapkal, S.D., Kakarwal, S.N., Revankar, P.S.: Analysis of Classification by Supervised and Unsupervised Learning. In: International Conference on Computational Intelligence and Multimedia Applications (2007)

    Google Scholar 

  9. Özbakir, L., Baykasoğlu, A., Tapkan, P.: Bees algorithm for generalized assignment problem. Applied Mathematics and Computation 215(11), 3782–3795 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  10. Shah, H., Ghazali, R., Nawi, N.M.: Hybrid Ant Bee Colony Algorithm for Volcano Temperature Prediction. In: Chowdhry, B.S., Shaikh, F.K., Hussain, D.M.A., Uqaili, M.A. (eds.) IMTIC 2012. CCIS, vol. 281, pp. 453–465. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  11. Shah, H., Ghazali, R., Mohmad Hassim, Y.M.: Honey Bees Inspired Learning Algorithm: Nature Intelligence Can Predict Natural Disaster. In: Herawan, T., Ghazali, R., Deris, M.M. (eds.) Recent Advances on Soft Computing and Data Mining SCDM 2014. AISC, vol. 287, pp. 215–225. Springer, Heidelberg (2014)

    Chapter  Google Scholar 

  12. Karaboga, D., Akay, B.: A comparative study of Artificial Bee Colony algorithm. Applied Mathematics and Computation 214(1), 108–132 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  13. Murtagh, F.: Multilayer perceptrons for classification and regression. Neurocomputing 2(5-6), 183–197 (1991)

    Article  MathSciNet  Google Scholar 

  14. Stromatias, E.: Developing a supervised training algorithm for limited precision feed-forward spiking neural networks, Liverpool University (2011)

    Google Scholar 

  15. Stork, D.G., Allen, J.D.: How to solve the N-bit parity problem with two hidden units. Neural Networks 5(6), 923–926 (1992)

    Article  Google Scholar 

  16. Minsky, M., Papert, S.A.: Perceptrons: An Introduction to Computational Geometry. MIT Press, Cambridge (1969)

    MATH  Google Scholar 

  17. Fahlman, S.: An empirical study of learning speed in backpropagation networks. Technical Report CMU-CS-88-162, Carnegie Mellon University, Pittsburgh, PA 15213 (September 1988)

    Google Scholar 

  18. United States Department of Commerce: National Oceanic and Atmospheric Administration (NOAA) (2012) (cited 2012 2012); National Data Buoy Center Bldg. 3205 Stennis Space Center, MS 39529 228-688-2805

    Google Scholar 

  19. Rosenblatt, F.: A Probabilistic Model for Information Storage and Organization in the Brain, pp. 386–408. Cornell Aeronautical Laboratory (1958)

    Google Scholar 

  20. Zurada, J.M.: Introduction to artificial neural systems, West (1992)

    Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  22. Shah, H., Ghazali, R., Herawan, T., Khan, N., Khan, M.S.: Hybrid Guided Artificial Bee Colony Algorithm for Earthquake Time Series Data Prediction. In: Shaikh, F.K., Chowdhry, B.S., Zeadally, S., Hussain, D.M.A., Memon, A.A., Uqaili, M.A. (eds.) IMTIC 2013. CCIS, vol. 414, pp. 204–215. Springer, Heidelberg (2014)

    Chapter  Google Scholar 

  23. Yang, S.D., Yi, Y.L., Shan, Z.Y.: Gbest-guided Artificial Chemical Reaction Algorithm for global numerical optimization. Procedia Engineering 24, 197–201 (2011)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Universiti Tun Hussein Onn Malaysia, Parit Raja, 86400, Batu Pahat, Johor, Malaysia

    Habib Shah, Rozaida Ghazali & Rashid Naseem

  2. University of Malaya, 50603, Lembah Pantai, Kuala Lumpur, Malaysia

    Tutut Herawan

  3. AMCS Research Center, Yogyakarta, Indonesia

    Tutut Herawan

  4. Deakin University, Waurn Ponds, Geelong, VIC, Australia

    Maslina Abdul Aziz & Jemal H. Abawajy

Authors
  1. Habib Shah
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tutut Herawan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rozaida Ghazali
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rashid Naseem
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Maslina Abdul Aziz
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jemal H. Abawajy
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Artificial Intelligence, Faculty of Computer Science and Information Technology Building, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Chu Kiong Loo

  2. Department of Electronics and Communication Engineering,College of Engineering, Jalan IKRAM-UNITEN, Universiti Tenaga Nasional, 43009, Kajang, Selangor, Malaysia

    Keem Siah Yap

  3. School of Engineering and Information Technology, Murdoch University, South St, 6150, Murdoch, Western Australia, Australia

    Kok Wai Wong

  4. Department of Electrical and Electronics Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, 120-749, Seoul, South Korea

    Andrew Teoh

  5. Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, Ren’ai Road 111, SIP 215123, Suzhou, Jiangsu Province, China

    Kaizhu Huang

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Shah, H., Herawan, T., Ghazali, R., Naseem, R., Aziz, M.A., Abawajy, J.H. (2014). An Improved Gbest Guided Artificial Bee Colony (IGGABC) Algorithm for Classification and Prediction Tasks. In: Loo, C.K., Yap, K.S., Wong, K.W., Teoh, A., Huang, K. (eds) Neural Information Processing. ICONIP 2014. Lecture Notes in Computer Science, vol 8834. Springer, Cham. https://doi.org/10.1007/978-3-319-12637-1_70

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-12637-1_70

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12636-4

  • Online ISBN: 978-3-319-12637-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation