Bird Mating Optimization Based Multilayer Perceptron for Diseases Classification

  • N. K. S. Behera
  • A. R. Routray
  • Janmenjoy Nayak
  • H. S. Behera
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 33)


Classification of data is a significant method of data analysis that can be used for intelligent decision making and neural networks are vital contrivances of such classification. Several meta-heuristic algorithms based neural network models such as genetic algorithm (GA) and differential evolution (DE) based MLP are efficiently implemented for this task. However these methods are trapped at local optima. To overcome such limitations, firefly algorithm (FA) and bird mating optimization (BMO) based MLP techniques have been proposed in the paper and tested over several bio-medical datasets like thyroid, hepatitis and heart diseases for classification. The result shows efficient classification of different patients into their diseases categories according to the data obtained from different pathological test.


Bird mating optimization Firefly algorithm Multilayer perceptron Liver disease Indian Pima diabetes disease 


  1. 1.
    Yang, X.S.: Firefly algorithm, stochastic test functions and design optimization. Int. J. Bio-inspired Comput. 2(2), 78–84 (2010)Google Scholar
  2. 2.
    Yang, X.S.: Firefly algorithm for multimodal optimization. In: Stochastic Algorithm: Foundation and Applications, SAGA 2009, Lecture Notes in Computer Sciences, vol. 5792, pp. 169–178 (2009)Google Scholar
  3. 3.
    Askarzadeh, A.: Bird mating optimizer: an optimization algorithm inspired by bird mating strategies. Commun. Nonlinear Sci. Numer. Simul. 19(4), 1213–1228 (2014)Google Scholar
  4. 4.
    Price, K.V., Glover, F., Corne, D., Dorigo, M.: An introduction to differential evolution new ideas in optimization, pp. 79–108. McGraw-Hill, London (2009)Google Scholar
  5. 5.
    Li, T.-S.: Feature selection for classification by using a GA-based neural network approach. J. Chin. Inst. Ind. Eng. 23(1), 55–64 (2006)Google Scholar
  6. 6.
    Askarzadeh, A., Rezazadeh, A.: Artificial neural network training using a new efficient optimization algorithm. Appl. Soft Comput. 13, 1206–1213 (2013)Google Scholar
  7. 7.
    Storn, R., Price, K.: Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11, 341–359 (2004)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Nayak J., Nanda, M., Nayak, K., Naik, B., Behera, H.S.: An Improved Firefly Fuzzy C-Means (FAFCM) Algorithm for Clustering Real World Data Sets, Smart Innovation, Systems and Technologies. Springer, Switzerland, vol. 27, pp. 339–348 (2014)Google Scholar
  9. 9.
    Charbonneau, P.: An Introduction to Genetic Algorithms for Numerical Optimization. CAR Technical Note, NCARGoogle Scholar
  10. 10.
    Schwefel, H.P.: Evolution and Optimum Seeking: The Sixth Generation. Wiley, New York (1999)Google Scholar
  11. 11.
    Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Springer, Berlin (2003)Google Scholar
  12. 12.
    Rezazadeh, A., Askarzadeh, A., Sedighizadeh, M.: ANN-based PEMFC modeling by a new learning algorithm. Int. Rev. Modell. Simul. 3(2), 187–193 (2010)Google Scholar
  13. 13.
    Askarzadeh, A., Rezazadeh, A.: Artificial immune system-based parameter extraction of proton exchange membrane fuel cell. Int. J. Electr. Power Energy Syst. 33, 933–938 (2011)CrossRefGoogle Scholar
  14. 14.
    Blum, C., Socha, K.: Training feed-forward neural networks with ant colony optimization: An application to pattern classification. In: Proceedings of the Fifth International Conference on Hybrid Intelligent Systems, pp. 233–238 (2005)Google Scholar
  15. 15.
    Shi, Y.-J., Teng, H.-F., Li Z.-Q.: Cooperative co-evolutionary differential evolution for function optimization. Adv. Nat. Comput. 3611, 1080–1088 (2005)Google Scholar
  16. 16.
    Yang, X.S., Deb, S.: Cuckoo search via L’evy flights. In: Proceedings of World Congress on Nature and Biologically Inspired Computing. IEEE Publications, USA pp. 210–214 (2009)Google Scholar

Copyright information

© Springer India 2015

Authors and Affiliations

  • N. K. S. Behera
    • 1
  • A. R. Routray
    • 2
  • Janmenjoy Nayak
    • 1
  • H. S. Behera
    • 1
  1. 1.Department of Computer Science and Engineering and Information TechnologyVeer Surendra Sai University of TechnologyBurlaIndia
  2. 2.Department of Computer Science and ApplicationsFakir Mohan UniversityBalasoreIndia

Personalised recommendations