The Effect of Bat Population in Bat-BP Algorithm

  • Nazri Mohd. Nawi
  • Muhammad Zubair Rehman
  • Abdullah Khan
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 291)


A new metaheuristic based back-propagation algorithm known as Bat-BP is presented in this paper. The proposed Bat-BP algorithm successfully solves the problems like slow convergence to global minima and network stagnancy in back-propagation neural network (BPNN) algorithm. In this paper, the bat population is increased from 10 to 500 bats to detect the performance decline or incline in the Bat-BP algorithm by performing simulations on XOR and OR datasets. The simulation results show that the convergence rate to global minimum in Bat-BP is directly proportional with an increase in bats on 2-bit XOR dataset. In case of 3-bit XOR and 4-bit OR datasets, the results deteriorated with an increase in the bat population.


Bat population Metaheuristics Slow convergence Global minima Bat algorithm Back-propagation neural network algorithm Bat-BP algorithm Momentum 



The Authors would like to thank Office of Research, Innovation, Commercialization and Consultancy Office (ORICC), Universiti Tun Hussein Onn Malaysia (UTHM) and Ministry of Higher Education (MOHE) Malaysia for financially supporting this Research under Fundamental Research Grant Scheme (FRGS) vote no. 1236.


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Copyright information

© Springer Science+Business Media Singapore 2014

Authors and Affiliations

  • Nazri Mohd. Nawi
    • 1
  • Muhammad Zubair Rehman
    • 1
  • Abdullah Khan
    • 1
  1. 1.Software and Multimedia Centre, Faculty of Computer Science and Information TechnologyUniversiti Tun Hussein Onn Malaysia (UTHM)Parit Raja, Batu PahatMalaysia

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