Parallel Hyperheuristics for the Antenna Positioning Problem

  • Carlos Segura
  • Yanira González
  • Gara Miranda
  • Coromoto León
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 79)


Antenna Positioning Problem (app) is an NP-Complete Optimisation Problem which arises in the telecommunication field. It consists in identifying the infrastructures required to establish a wireless network. Several objectives must be considered when tackling app and multi-objective evolutionary algorithms have been successfully applied to solve it. However, they required a deep analysis, and a correct parameterisation in order to obtain high quality solutions. In this work, a parallel hyperheuristic island-based model approach is presented. Several hyperheuristic scoring strategies are tested. Results show the advantages of the parallel hyperheuristic. On one hand, the testing of each sequential configuration can be avoided. On the other hand, it speeds up the attainment of high-quality solutions even when compared with the best sequential approaches.


ParallelHyperheuristics Antenna Positioning Problem Multi-Objective Evolutionary Algorithms 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Alba, E.: Evolutionary algorithms for optimal placement of antennae in radio network design. In: Parallel and Distributed Processing Symposium, International, vol. 7, p. 168 (2004),
  2. 2.
    Cantú-Paz, E.: A survey of parallel genetic algorithms. Calculateurs Paralleles 10 (1998)Google Scholar
  3. 3.
    Gómez-Pulido, J.: Web site of net-centric optimization,
  4. 4.
    Holland, J.H.: Adaptation in natural and artificial systems. MIT Press, Cambridge (1992)Google Scholar
  5. 5.
    León, C., Miranda, G., Segura, C.: Hyperheuristics for a Dynamic-Mapped Multi-Objective Island-Based Model. In: Omatu, S., Rocha, M.P., Bravo, J., Fernández, F., Corchado, E., Bustillo, A., Corchado, J.M. (eds.) IWANN 2009. LNCS, vol. 5518, pp. 41–49. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  6. 6.
    Mendes, S.P., Molina, G., Vega-Rodríguez, M.A., Gomez-Pulido, J.A., Sáez, Y., Miranda, G., Segura, C., Alba, E., Isasi, P., León, C., Sánchez-Pérez, J.M.: Benchmarking a Wide Spectrum of Meta-Heuristic Techniques for the Radio Network Design Problem. IEEE Transactions on Evolutionary Computation, 1133–1150 (2009)Google Scholar
  7. 7.
    Mendes, S.P., Pulido, J.A.G., Rodriguez, M.A.V., Simon, M.D.J., Perez, J.M.S.: A differential evolution based algorithm to optimize the radio network design problem. In: E-SCIENCE 2006: Proceedings of the Second IEEE International Conference on e-Science and Grid Computing, p. 119. IEEE Computer Society, Washington (2006), CrossRefGoogle Scholar
  8. 8.
    Meunier, H., Talbi, E.G., Reininger, P.: A multiobjective genetic algorithm for radio network optimization. In: Proceedings of the 2000 Congress on Evolutionary Computation, pp. 317–324. IEEE Press, Los Alamitos (2000)Google Scholar
  9. 9.
    Segura, C., González, Y., Miranda, G., León, C.: A Multi-Objective Evolutionary Approach for the Antenna Positioning Problem. In: 14th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems. LNCS (LNAI), Springer, Heidelberg (to appear, 2010) Google Scholar
  10. 10.
    Talbi, E.G., Meunier, H.: Hierarchical parallel approach for gsm mobile network design. J. Parallel Distrib. Comput. 66(2), 274–290 (2006)zbMATHCrossRefGoogle Scholar
  11. 11.
    Tcha, D.w., Myung, Y.S., Kwon, J.h.: Base station location in a cellular CDMA system. Telecommunication Systems 14(1-4), 163–173 (2000)zbMATHCrossRefGoogle Scholar
  12. 12.
    Vasquez, M., Hao, J.K.: A heuristic approach for antenna positioning in cellular networks. Journal of Heuristics 7(5), 443–472 (2001), zbMATHCrossRefGoogle Scholar
  13. 13.
    Vinkó, T., Izzo, D.: Learning the best combination of solvers in a distributed global optimization environment. In: Proceedings of Advances in Global Optimization: Methods and Applications (AGO), Mykonos, Greece, pp. 13–17 (2007)Google Scholar
  14. 14.
    Weicker, N., Szabo, G., Weicker, K., Widmayer, P.: Evolutionary multiobjective optimization for base station transmitter placement with frequency assignment. IEEE Transactions on Evolutionary Computation 7(2), 189–203 (2003)CrossRefGoogle Scholar
  15. 15.
    Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the Strength Pareto Evolutionary Algorithm for Multiobjective Optimization. Evolutionary Methods for Design, Optimization and Control, 19–26 (2002)Google Scholar
  16. 16.
    Zitzler, E., Thiele, L.: Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol. 1498, pp. 292–301. Springer, Heidelberg (1998), CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Carlos Segura
    • 1
  • Yanira González
    • 1
  • Gara Miranda
    • 1
  • Coromoto León
    • 1
  1. 1.Dpto. Estadística, I. O. y ComputaciónUniversidad de La LagunaSanta Cruz de TenerifeSpain

Personalised recommendations