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International Conference on Neural Information Processing

ICONIP 2014: Neural Information Processing pp 237–244Cite as

Neural Network Training by Hybrid Accelerated Cuckoo Particle Swarm Optimization Algorithm

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8835))

Abstract

Metaheuristic algorithm is one of the most popular methods in solving many optimization problems. This paper presents a new hybrid approach comprising of two natures inspired metaheuristic algorithms i.e. Cuckoo Search (CS) and Accelerated Particle Swarm Optimization (APSO) for training Artificial Neural Networks (ANN). In order to increase the probability of the egg’s survival, the cuckoo bird migrates by traversing more search space. It can successfully search better solutions by performing levy flight with APSO. In the proposed Hybrid Accelerated Cuckoo Particle Swarm Optimization (HACPSO) algorithm, the communication ability for the cuckoo birds have been provided by APSO, thus making cuckoo bird capable of searching for the best nest with better solution. Experimental results are carried-out on benchmarked datasets, and the performance of the proposed hybrid algorithm is compared with Artificial Bee Colony (ABC) and similar hybrid variants. The results show that the proposed HACPSO algorithm performs better than other algorithms in terms of convergence and accuracy.

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Author information

Authors and Affiliations

  1. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, 86400, Parit Raja, Johor, Malaysia

    Nazri Mohd Nawi, Abdullah khan & M. Z. Rehman

  2. School of Information Technology, Deakin University, Waurn Ponds, Geelong, VIC, Australia

    Maslina Abdul Aziz & Jemal H. Abawajy

  3. University of Malaya, 50603, Kuala Lumpur, Malaysia

    Tutut Herawan

  4. AMCS Research Center, Yogyakarta, Indonesia

    Tutut Herawan

Authors
  1. Nazri Mohd Nawi
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  2. Abdullah khan
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  3. M. Z. Rehman
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  4. Maslina Abdul Aziz
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  5. Tutut Herawan
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  6. Jemal H. Abawajy
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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, Universiti Tenaga Nasional, Jalan IKRAM-UNITEN, 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

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© 2014 Springer International Publishing Switzerland

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Nawi, N.M., khan, A., Rehman, M.Z., Aziz, M.A., Herawan, T., Abawajy, J.H. (2014). Neural Network Training by Hybrid Accelerated Cuckoo Particle Swarm Optimization Algorithm. 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 8835. Springer, Cham. https://doi.org/10.1007/978-3-319-12640-1_29

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  • DOI: https://doi.org/10.1007/978-3-319-12640-1_29

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12639-5

  • Online ISBN: 978-3-319-12640-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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