Chapter 9. Applying Evolutionary Algorithms to Solve the Automatic Frequency Planning Problem

  1. Amiya Nayak B.Math., Ph.D. Adjunct Research Professor Associate Editor Full Professor3,
  2. Ivan Stojmenović Ph.D. Chair Professor founder editor-in-chief3,4
  1. Francisco Luna1,
  2. Enrique Alba2,
  3. Antonio J. Nebro1,
  4. Patrick Mauroy1,
  5. Salvador Pedraza1

Published Online: 1 MAR 2007

DOI: 10.1002/9780470175668.ch9

Book Title

Handbook of Applied Algorithms: Solving Scientific, Engineering and Practical Problems

Handbook of Applied Algorithms: Solving Scientific, Engineering and Practical Problems

Additional Information

How to Cite

Luna, F., Alba, E., Nebro, A. J., Mauroy, P. and Pedraza, S. (2007) Applying Evolutionary Algorithms to Solve the Automatic Frequency Planning Problem, in Handbook of Applied Algorithms: Solving Scientific, Engineering and Practical Problems (eds A. Nayak and I. Stojmenović), John Wiley & Sons, Inc., Hoboken, NJ, USA. doi: 10.1002/9780470175668.ch9

Editor Information

  1. 3

    SITE, University of Ottawa, 800 King Edward Ave., Ottawa, ON K1N 6N5, Canada

  2. 4

    EECE, University of Birmingham, UK

Author Information

  1. 1

    Universidad de Málaga, ETS. Ing. Informática, Campus de Teatinos, 29071 Málaga, Spain

  2. 2

    Dpto. de Lenguajes y Ciencias de la Computación, E.T.S. Ing. Informática, Campus de Teatinos, 29071 Málaga, Spain

Publication History

  1. Published Online: 1 MAR 2007
  2. Published Print: 14 FEB 2008

ISBN Information

Print ISBN: 9780470044926

Online ISBN: 9780470175668

SEARCH

CHAPTER TOOLS

Get PDF (230K)

Keywords:

  • automatic frequency planning (AFP) and frequency assignment problem (FAP);
  • evolutionary algorithm;
  • EA initialization and perturbation methods

Summary

Frequency assignment is a well-known problem in operations research for which different mathematical models exist depending on the application-specific conditions. However, most of these models are far from considering actual technologies currently deployed in GSM networks, such as frequency hopping. In these networks, interferences provoked by channel reuse due to the limited available radio spectrum result in a major impact of the quality of service (QoS) for subscribers. Therefore, frequency planning is of great importance for GSM operators. We here focus on optimizing the frequency planning of a realistic-sized, real-world GSM network by using evolutionary algorithms (EAs). Results show that a (1+10) EA developed by the chapter authors for which different seeding methods and perturbation operators have been analyzed is able to compute accurate and efficient frequency plans for real-world instances.

Get PDF (230K)