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Hybrid Global Crossover Bees Algorithm for Solving Boolean Function Classification Task

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Intelligent Computing Methodologies (ICIC 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10363))

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Abstract

Using typical algorithms for training multilayer perceptron (MLP) creates some difficulties like slow convergence speed and local minima trapping in the solution space. Bio-inspired learning algorithms are famous for solving linear and nonlinear combinatorial problems. Artificial Bee Colony (ABC) algorithm is one among the famous bio-inspired algorithms. However, due to slow exploration process, it has been focused by researchers for further enhancement in optimization area. Therefore, this paper proposed a new hybrid swarm based learning algorithm called Global Crossover Artificial Bee Colony (GCABC) algorithm for training MLP for solving boolean classification problems. The simulation results of proposed GCABC algorithm compared with standard bio-inspired algorithms such as ABC, and Global Artificial Bee Colony (GABC) show that the proposed algorithm is achievable and efficient results in benchmark boolean function classification, with fast convergence speed.

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Author information

Authors and Affiliations

  1. College of Computer Science, Department of Computer Science, King Khalid University, Abha, Kingdom of Saudi Arabia

    Habib Shah & Nasser Tairan

  2. Department of Mathematics, Kohat University of Science and Technology, Kohat, Khyber Pukhtunkhwa, Pakistan

    Wali Khan Mashwani & Muhammad Asif Jan

  3. Faculty of Computer Systems and Software Engineering (FSKKP), Universiti Malaysia Pahang, Malaysia, Malaysia

    Abdulrahman Ahmad Al-Sewari

  4. Department of Computer Science, Abdul Wali Khan University, Timergara Campus, Mardan, Khyber Pukhtunkhwa, Pakistan

    Gran Badshah

Authors
  1. Habib Shah
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  2. Nasser Tairan
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  3. Wali Khan Mashwani
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  4. Abdulrahman Ahmad Al-Sewari
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  5. Muhammad Asif Jan
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  6. Gran Badshah
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Corresponding author

Correspondence to Habib Shah .

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Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. Liverpool John Moores University, Liverpool, United Kingdom

    Abir Hussain

  3. Inha University, Incheon, Korea (Republic of)

    Kyungsook Han

  4. Indian Institute of Technology Madras, Chennai, India

    M. Michael Gromiha

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Shah, H., Tairan, N., Mashwani, W.K., Al-Sewari, A.A., Jan, M.A., Badshah, G. (2017). Hybrid Global Crossover Bees Algorithm for Solving Boolean Function Classification Task. In: Huang, DS., Hussain, A., Han, K., Gromiha, M. (eds) Intelligent Computing Methodologies. ICIC 2017. Lecture Notes in Computer Science(), vol 10363. Springer, Cham. https://doi.org/10.1007/978-3-319-63315-2_41

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  • DOI: https://doi.org/10.1007/978-3-319-63315-2_41

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63314-5

  • Online ISBN: 978-3-319-63315-2

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