Skip to main content
SpringerLink
Log in

An alternative approach to neural network training based on hybrid bio meta-heuristic algorithm

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

Abstract

Metaheuristic algorithms are popular techniques used to solve several optimization problems. Among the key algorithms, cuckoo search (CS) is a comparatively novel and promising metaheuristic algorithm. Various researchers have shown that it performs better when compared to other metaheuristic algorithms while searching for optimal value and is being used to solve various real-world problems. However, the basic CS algorithm can be improved by enhancing the probabilities of survival of the eggs. It will decrease the possibility of the eggs getting ruined by the host bird. The cuckoo birds move to a new position looking for more search space to get better solutions. Furthermore, better search space can be obtained by executing levy flight with accelerated particle swarm optimization (APSO). This research proposes a new method known as hybrid accelerated cuckoo particle swarm optimization (HACPSO) algorithm, based on two metaheuristic algorithms. In the proposed HACPSO algorithm, APSO provides communication for looking better place having the best nest with greater survivability for cuckoo birds. Different simulation has been carried using standard dataset and efficiency of the proposed algorithm is compared with CS, artificial bee colony and other similar hybrid variants. The simulation results demonstrate that the HACPSO algorithm performs better as compared to other algorithms in term of accuracy, MSE, SD, and with fast convergence rate to the target space.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • Abid S, Fnaiech F, Najim M (2001) A fast feedforward training algorithm using a modified form of the standard backpropagation algorithm. IEEE Trans Neural Netw 12(2):424–430

    Article  Google Scholar 

  • Blum C, Socha K (2005) Training feed-forward neural networks with ant colony optimization: an application to pattern classification. In: Fifth international conference on hybrid intelligent systems (HIS'05), Rio de Janeiro, Brazil, 6–9 November 2005. IEEE

  • Chaowanawatee K, Heednacram A (2012) Implementation of cuckoo search in RBF neural network for flood forecasting. Paper presented at the 2012 fourth international conference on computational intelligence, communication systems and networks

  • Eberhart R, Kennedy J (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks, Piscataway, NJ, pp 1942–1948

  • Gori M, Tesi A (1992) On the problem of local minima in backpropagation. IEEE Trans Pattern Anal Mach Intell 14(1):76–86

    Article  Google Scholar 

  • Karaboga D, Ozturk C (2009) Neural networks training by artificial bee colony algorithm on pattern classification. Neural Netw World 19(3):279

    Google Scholar 

  • Kennedy J, Eberhart R (2001) Swarm intelligence. Academic Press, London

    Google Scholar 

  • Kumbhar PY, Krishnan S (2011) Use of artificial bee colony (ABC) algorithm in artificial neural network synthesis. Int J Adv Eng Sci Technol 11(1):162–171

    Google Scholar 

  • Langin C, Rahimi S (2010) Soft computing in intrusion detection: the state of the art. J Ambient Intell Humaniz Comput 1(2):133–145

    Article  Google Scholar 

  • Nawi NM, Ghazali R, Salleh MNM (2010a) The development of improved back-propagation neural networks algorithm for predicting patients with heart disease. Paper presented at the international conference on information computing and applications

  • Nawi NM, Ransing R, Hamid NA (2010b) BPGD-AG: a new improvement of back-propagation neural network learning algorithms with adaptive gain. J Sci Technol 2(2):83–102

    Google Scholar 

  • Nawi NM, Ransing RS, Salleh MNM, Ghazali R, Hamid NA (2010c) An improved back propagation neural network algorithm on classification problems. In: Database theory and application, bio-science and bio-technology. Springer, Berlin, pp 177–188

  • Nawi NM, Khan A, Rehman MZ (2013a) A new back-propagation neural network optimized with cuckoo search algorithm. In: ICCSA-2013 in LNCS journal of Springer. Vietnam part I, LNCS, vol 7971, no 1, pp 413–426

  • Nawi NM, Khan A, Rehman M (2013b) A new Levenberg Marquardt based back propagation algorithm trained with cuckoo search. Procedia Technol 11:18–23

    Article  Google Scholar 

  • Nawi NM, Khan A, Rehman MZ (2013c) A new cuckoo search based Levenberg–Marquardt (CSLM) algorithm. Paper presented at the international conference on computational science and its applications

  • Nawi NM, Rehman M, Aziz MA, Herawan T, Abawajy JH (2014) Neural network training by hybrid accelerated cuckoo particle swarm optimization algorithm. Paper presented at the international conference on neural information processing

  • Pavlyukevich I (2007) Lévy flights, non-local search and simulated annealing. J Comput Phys 226(2):1830–1844

    Article  MathSciNet  MATH  Google Scholar 

  • Prabukumar M, Agilandeeswari L, Ganesan K (2019) An intelligent lung cancer diagnosis system using cuckoo search optimization and support vector machine classifier. J Ambient Intell Humaniz Comput 10(1):267–293

    Article  Google Scholar 

  • Rehman M, Nawi N (2011) Improving the accuracy of gradient descent back propagation algorithm (GDAM) on classification problems. Int J New Comput Archit Appl 1(4):838–847

    Google Scholar 

  • Rehman MZ, Nawi MN, Khan A (2013) A new bat based back-propagation (BAT-BP) algorithm. In: (ICSS 2013) Proceedings to be published in the Springer series advances in intelligent systems and computing, vol 240, pp 395–404

  • Sexton JJRDR (1999) Optimization of neural networks: a comparative analysis of the genetic algorithm and simulated annealing. Eur J Oper Res 114:589–601

    Article  MATH  Google Scholar 

  • Tuba M, Subotic M, Stanarevic N (2011) Modified cuckoo search algorithm for unconstrained optimization problems. Paper presented at the proceedings of the 5th European conference on European computing conference

  • Walton S, Hassan O, Morgan K, Brown M (2011) Modified cuckoo search: a new gradient free optimisation algorithm. Chaos Solitons Fractals 44(9):710–718

    Article  Google Scholar 

  • Williams RJ, Peng J (1990) An efficient gradient-based algorithm for on-line training of recurrent network trajectories. Neural Comput 2(4):490–501

    Article  Google Scholar 

  • Yang X-S (2010a) Engineering optimization: an introduction with metaheuristic applications. Wiley, New York

    Book  Google Scholar 

  • Yang X-S (2010b) Nature-inspired metaheuristic algorithms. Luniver Press, Frome

    Google Scholar 

  • Yang X, Deb S (2009) Cuckoo search via Lévy flights. In: Proceedings of world congress on nature and biologically inspired computing (NaBIC), India

  • Yang XS, Deb S (2010) Engineering optimisation by cuckoo search. Int J Math Model Numer Optim 1(4):330–343

    MATH  Google Scholar 

  • Yang X-S, Deb S, Fong S (2011) Accelerated particle swarm optimization and support vector machine for business optimization and applications. Paper presented at the international conference on networked digital technologies

  • Yu X, Efe MO, Kaynak O (2002) A general backpropagation algorithm for feedforward neural networks learning. IEEE Trans Neural Netw 13(1):251–254

    Article  Google Scholar 

  • Zhang J-R, Zhang J, Lok T-M, Lyu MR (2007) A hybrid particle swarm optimization back propagation algorithm for neural network training. Appl Math Comput 185:1026–1037

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Business and Management Sciences, Agriculture University, Peshawar, Pakistan

    Abdullah Khan, Muhammad Imran, Asfandyar Khan & Javed Iqbal Bangash

  2. Department of Computer Science, CECOS University, Peshawar, Pakistan

    Rahmat Shah

  3. Department of Computer Science, University of Science and Technology, Bannu, Pakistan

    Khalid Shah

Authors
  1. Abdullah Khan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rahmat Shah
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Muhammad Imran
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Asfandyar Khan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Javed Iqbal Bangash
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Khalid Shah
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Muhammad Imran.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Khan, A., Shah, R., Imran, M. et al. An alternative approach to neural network training based on hybrid bio meta-heuristic algorithm. J Ambient Intell Human Comput 10, 3821–3830 (2019). https://doi.org/10.1007/s12652-019-01373-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-019-01373-4

Keywords

Search

Navigation