Application of Differential Evolution to a Multi-Objective Real-World Frequency Assignment Problem

  • Marisa Silva Maximiano
  • Miguel A. Vega-Rodríguez
  • Juan A. Gómez-Pulido
  • Juan M. Sánchez-Pérez
Part of the Evolutionary Learning and Optimization book series (ALO, volume 4)


Frequency spectrum is one of the scarcest resources for any mobile operator. Frequencies have to be reused throughout the network. Consequently, interferences may occur and some separation constraints may be violated. Frequency assignment problem (FAP) aims to use effectively the available frequency spectrum to minimize interferences by carefully allocating available frequencies to existing base stations [1]. It is a very demanding problem in telecommunications, especially in GSM networks [2], even though it is very time-consuming. It is one of the most fundamental problems in mobile communications planning. A good FAP solution leads to better network quality and increased capacity without sacrificing quality of service (QoS) for all users of the mobile network.


Differential Evolution Pareto Front Pareto Solution Frequency Assignment Problem Adjacent Channel Interference 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Eisenblätter, A.: Frequency assignment in GSM networks: Models, heuristics and lower bounds, Ph.D. Thesis, Technische Universität Berlin (June 2001)Google Scholar
  2. 2.
    GSM Association, GSM World, (last accessed January 2010)
  3. 3.
    Shirazi, S.A.G., Amindavar, H.: Fixed Channel assignment using new dynamic programming approach in cellular radio networks. Computers Electrical Engineering 31(4-5), 303–333 (2005)zbMATHCrossRefGoogle Scholar
  4. 4.
    Storn, R., Price, K.V.: Differential Evolution - A Simple and Efficient Adaptive Scheme for Global Optimization over Continuous Spaces, Technical Report TR-95-012, International Computer Science Insitute (March 1995)Google Scholar
  5. 5.
    Storn, R., Price, K.V.: Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optimization 11(4), 341–359 (1997)zbMATHCrossRefMathSciNetGoogle Scholar
  6. 6.
    Price, K.V., Storn, R.M., Lampinen, J.A.: Differential Evolution: a Practical Approach to Global Optimization. Springer, Berlin (2005)zbMATHGoogle Scholar
  7. 7.
    Qing, A.: Differential Evolution: Fundamentals and Applications in Electrical Engineering. John Wiley, New York (2009)Google Scholar
  8. 8.
    Storn, R.: Differential evolution (DE) for continuous function optimization (an algorithm by Kenneth Price and Rainer Storn), (last accessed October 23, 2009)
  9. 9.
    Fonseca, C.M., Paquete, L., López-Ibáñez, M.: An improved dimension-sweep algorithm for the hypervolume indicator. In: IEEE Congress Evolutionary Computation, Vancouver, BC, Canada, July 16-21, pp. 1157–1163 (2006)Google Scholar
  10. 10.
    Zitzler, E., Thiele, L.: Multiobjective optimization using evolutionary algorithms - A comparative case study. In: 5th Int. Conf. Parallel Problem Solving Nature, Amsterdam, The Netherlands, September 27-30, pp. 292–304 (1998)Google Scholar
  11. 11.
    Kuurne, A.M.J.: On GSM mobile measurement based interference matrix generation. In: IEEE 55th Vehicular Technology Conf., Birmingham, AL, May 6-9, vol. 4, pp. 1965–1969 (2002)Google Scholar
  12. 12.
    Luna, F., Estébanez, C., León, C., Chaves-González, J.M., Alba, E., Aler, R., Segura, C., Vega-Rodríguez, M.A., Nebro, A.J., Valls, J.M., Miranda, G., Gómez-Pulido, G.A.: Metaheuristics for solving a real-world frequency assignment problem in GSM networks. In: Genetic evolutionary computation Conf., Atlanta, GA, July 12-16, pp. 1579–1586 (2008)Google Scholar
  13. 13.
    Mishra, A.R.: Fundamentals of Cellular Network Planning and Optimisation: 2G/2.5G/3G...Evolution to 4G. John Wiley, New York (2004)CrossRefGoogle Scholar
  14. 14.
    Luna, F., Blum, C., Alba, E., Nebro, A.J.: ACO vs EAs for solving a real-world frequency assignment problem in GSM networks. In: Genetic Evolutionary Computation Conf., London, UK, July 7-11, pp. 94–101 (2007)Google Scholar
  15. 15.
    Maximiano, M., Vega-Rodríguez, M.A., Gómez-Pulido, J.A., Sánchez-Pérez, J.M.: Parameter analysis for differential evolution with Pareto tournaments in a multiobjective FAP. In: Corchado, E., Yin, H. (eds.) IDEAL 2009. LNCS, vol. 5788, pp. 799–806. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  16. 16.
    Weicker, N., Szabo, G., Weicker, K., Widmayer, P.: Evolutionary multiobjective optimization for base station transmitter placement with frequency assignment. IEEE Trans. Evolutionary Computation 7(2), 189–203 (2003)CrossRefGoogle Scholar
  17. 17.
    Hansen, P., Mladenovic, N.: Variable neighborhood search: Principles and applications. European J. Operational Research 130(3), 449–467 (2001)zbMATHCrossRefMathSciNetGoogle Scholar
  18. 18.
    Hansen, P., Mladenovic, N., Pérez, J.A.M.: Variable neighbourhood search. Computers Operations Research 24(11), 1097–1100 (1997)zbMATHCrossRefMathSciNetGoogle Scholar
  19. 19.
    Maximiano, M., Vega-Rodríguez, M.A., Gómez-Pulido, J.A., Sánchez-Pérez, J.M.: A hybrid differential evolution algorithm to solve a real-world frequency assignment problem. In: Int. Multiconference Computer Science Information Technology, Polskie Towarzystwo Informatyczne, Wisla, Poland, October 20-22, pp. 201–205 (2008)Google Scholar
  20. 20.
    Maximiano, M., Vega-Rodríguez, M.A., Gómez-Pulido, J.A., Sánchez-Pérez, J.M.: Analysis of parameter settings for differential evolution algorithm to solve a real-world frequency assignment problem in GSM networks. In: 2nd Int. Conf. Advanced Engineering Computing Applications Sciences, Valencia, Spain, September 29-October 4, pp. 77–82 (2008)Google Scholar
  21. 21.
    Maximiano, M., Vega-Rodríguez, M.A., Gómez-Pulido, J.A., Sánchez-Pérez, J.M.: Multiobjective frequency assignment problem using the MO-VNS and MO-SVNS algorithms. In: World Congress Nature Biologically Inspired Computing, Coimbatore, India, December 9-11, pp. 221–226 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Marisa Silva Maximiano
    • 1
  • Miguel A. Vega-Rodríguez
    • 2
  • Juan A. Gómez-Pulido
    • 2
  • Juan M. Sánchez-Pérez
    • 2
  1. 1.Department of Informatic Engineering, School of Technology and ManagementPolytechnic Institute of LeiriaLeiriaPortugal
  2. 2.Department of Technologies of Computers and Communications, Escuela PolitécnicaUniversity of ExtremaduraCáceresSpain

Personalised recommendations