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SCH-EGA: An Efficient Hybrid Algorithm for the Frequency Assignment Problem

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Engineering Applications of Neural Networks (EANN 2013)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 383))

Abstract

This paper proposes a hybrid stochastic competitive Hopfield neural network-efficient genetic algorithm (SCH-EGA) approach to tackle the frequency assignment problem (FAP). The objective of FAP is to minimize the cochannel interference between satellite communication systems by rearranging the frequency assignments so that they can accommodate the increasing demands. In fact, as SCH-EGA algorithm owns the good adaptability, it can not only deal with the frequency assignment problem, but also cope with the problems of clustering, classification, the maximum clique problem and so on. In this paper, we first propose five optimal strategies to build an efficient genetic algorithm(EGA) which is the component of our hybrid algorithm. Then we explore different hybridizations between the Hopfield neural network and EGA. With the help of hybridization, SCH-EGA makes up for the defects in the Hopfield neural network and EGA while fully using the advantages of the two algorithms.

This research was partially supported by the grants from the Natural Science Foundation of China (No.61003205); the Qianjiang Talent Project of Zhejiang Province (No.2011R10087).

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Author information

Authors and Affiliations

  1. Multimedia Computing Lab, School of Information, Renmin University of China, China

    Shaohui Wu, Gang Yang, Jieping Xu & Xirong Li

Authors
  1. Shaohui Wu
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  2. Gang Yang
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  3. Jieping Xu
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  4. Xirong Li
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Editor information

Editors and Affiliations

  1. Department of Forestry & Management of the Environment and Natural Resources, Democritus University of Thrace, GR-68200, Orestiada, Hellas

    Lazaros Iliadis

  2. Frederick University of Cyprus, Cyprus

    Harris Papadopoulos

  3. Faculty of Engineering and Computing, Coventry University, UK

    Chrisina Jayne

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© 2013 Springer-Verlag Berlin Heidelberg

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Wu, S., Yang, G., Xu, J., Li, X. (2013). SCH-EGA: An Efficient Hybrid Algorithm for the Frequency Assignment Problem. In: Iliadis, L., Papadopoulos, H., Jayne, C. (eds) Engineering Applications of Neural Networks. EANN 2013. Communications in Computer and Information Science, vol 383. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41013-0_4

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  • DOI: https://doi.org/10.1007/978-3-642-41013-0_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-41012-3

  • Online ISBN: 978-3-642-41013-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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