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A Hybrid Grouping Genetic Algorithm for the Multiple-Type Access Node Location Problem

  • O. Alonso-Garrido
  • S. Salcedo-Sanz
  • L. E. Agustín-Blas
  • E. G. Ortiz-García
  • A. M. Pérez-Bellido
  • J. A. Portilla-Figueras
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5788)

Abstract

This paper presents a new model for the access node location problem (ANLP) in communications networks, in which the access nodes (concentrators) can be chosen from different types (with different capacity and cost). The paper also proposes a hybrid grouping genetic algorithm which is able to efficiently solve the problem. In the paper, the main characteristics of the algorithm (encoding, operators and fitness function) are fully described, and its performance has been shown by solving different ANLP instances incorporating the new model.

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References

  1. 1.
    Salcedo-Sanz, S., Portilla-Figueras, J.A., Ortiz-García, E.G., Pérez-Bellido, A.M., Thraves, C., Fernandez-Anta, A., Yao, X.: Optimal switch location in mobile communication networks using hybrid genetic algorithms. Applied Soft Computing 8(4), 1486–1497 (2008)CrossRefGoogle Scholar
  2. 2.
    El-Alfy, E.S.: Applications of genetic algorithms to optimal multilevel design of MPLS-based networks. Computer Communications 30, 2010–2020 (2007)CrossRefGoogle Scholar
  3. 3.
    Krishnamachari, B., Wicker, S.: Base station location optimization in cellular wireless networks using heuristic search algorithms. In: Wang, L. (ed.) Soft Computing in Communications. Springer, Heidelberg (2003)Google Scholar
  4. 4.
    Menon, S., Gupta, R.: Assigning cells to switches in cellular networks by incorporating a pricing mechanism into simulated annealing. IEEE Trans. Syst. Man Cybern. B 34(1), 558–565 (2004)CrossRefGoogle Scholar
  5. 5.
    Abuali, F.N., Schoenefeld, D.A., Wainwright, R.L.: Terminal assignment in a communications network using genetic algorithms. In: Proc. 22nd Annual ACM Computer Science Conference, pp. 74–81. ACM Press, New York (1994)Google Scholar
  6. 6.
    Falkenauer, E.: The grouping genetic algorithm–widening the scope of the GAs. Proc. of the Belgian journal of operations research, statistics and computer science 33, 79–102 (1992)zbMATHGoogle Scholar
  7. 7.
    Santos-Correa, E., Steiner, M.T., Freitas, A.A., Carnieri, C.: A genetic algorithm for solving a capacitated P-median problem. Numerical Algorithms 35(4), 373–388 (2004)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • O. Alonso-Garrido
    • 1
  • S. Salcedo-Sanz
    • 1
  • L. E. Agustín-Blas
    • 1
  • E. G. Ortiz-García
    • 1
  • A. M. Pérez-Bellido
    • 1
  • J. A. Portilla-Figueras
    • 1
  1. 1.Department of Signal Theory and CommunicationsUniversidad de AlcaláMadridSpain

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