An External Memory Supported ACO for the Frequency Assignment Problem

  • Adnan Acan
  • Akin Günay
Conference paper


Ant colony optimization algorithm is integrated with an external memory for the purpose of improving its efficiency for the solution of a well-known hard combinatorial optimization problem. The external memory keeps variable-size solution segments extracted from promising solutions of previous iterations. Each solution segment is associated with its parent’s fitness value. In the construction of a solution, each ant retrieves a segment from the memory using tournament selection and constructs a complete solution by filling the absent components. The proposed approach is used for the solution of minimum span frequency assignment problem for which very promising results are obtained for provably difficult benchmark test problems that could not be handled by any other ACO-based approach so far.


Problem Instance Channel Assignment External Memory Elite Solution Demand Vector 
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  1. [1]
    Dorigo, M., Caro, G.D., Gambardella, L.M. (1999) Ant algorithms for distributed discrete optimization, Artificial Life, Vol. 5, pp. 137–172.CrossRefGoogle Scholar
  2. [2]
    Louis, S. J., Johnson, J. (1997) Solving similar problems using genetic algorithms and case-based memory,. In: Back, T. (ed.) Proceedings of the Seventh International Conference on Genetic Algorithms — 1997. San Fransisco, CA, pp. 84–91.Google Scholar
  3. [3]
    Simoes, A., Costa, E. (2002) Using genetic algorithms to deal with dynamical environments: comparative study of several approaches based on promoting diversity. In: Langton, W. B. et al. (eds.) Proceedings of the genetic and evolutionary computation conference — GECCO 2002. Morgan Kaufmann, New York, pp.698–105.Google Scholar
  4. [4]
    Acan, A., Tekol, Y. (2003) Chromosome reuse in genetic algorithms. In: Cantu-Paz et al. (eds.) Genetic and Evolutionary Computation Conference — GECCO 2003. Springer-Verlag, Chicago, pp.695–705.Google Scholar
  5. [5]
    Goldberg, D. E., Smith, R. E. (1987) Non-stationary function optimization using genetic algorithms and with dominance and diploidy, Genetic Algorithms and their Applcations: Proceedings of the Second International Conference on Genetic Algorithms, pp. 217–223.Google Scholar
  6. [6]
    Montgomery, J., Randall, M. (20023) The accumulated experience ant colony for the travelling salesman problem, International Journal of Computational Intelligence and Applications, Vol.3, No. 2, World Scientific Publishing Company, pp. 189–198.CrossRefGoogle Scholar
  7. [7]
    Guntsch, M., Middendorf, M. (2002) A population based approach for ACO. In: Cagnoni, S. et al. (eds.) Applications of Evolutionary Computing — EvoWorkshops 2002, Lecture Notes in Computer Science, No:2279, Springer Verlag, pp. 72–81.Google Scholar
  8. [8]
    Guntsch, M., Middendorf, M. (2002) Applying population based ACO for dynamic optimization problems. In: Dorigo, M. et al. (eds.) Ant Algorithms — Third International Workshop ANTS 2002, Lecture Notes in Computer Science, No:2463, Springer Verlag, pp. 111–122.Google Scholar
  9. [9]
    Beckmann, D., Killat, (1999) U. A new strategy for the application genetic algorithms to the channel assignment problem, IEEE Trans. On Vehicular Technology, Vol. 48, No. 4, pp. 1261–1269.CrossRefGoogle Scholar
  10. [10]
    Maniezzo, V., Carbonaro, A. (1998) An ant heuristic for the frequency assignment problem, Technical Report, University of Bologna.Google Scholar
  11. [11]
    Montemanni, R., Smith, D.H., Allen, S.M. (2002) An ANTS algorithm for the minimum-span frequency assignment problem with multiple interference, IEEE Trans. on Vehicular Technology, Vol. 51, No. 5, pp. 949–953.CrossRefGoogle Scholar
  12. [12]
    Maniezzo, V.: (1999) Exact and approximate nondeterministic tree-search procedures for the quadratic assignment problem, Inform. Journal of Computing, Vol. 11, No. 4, pp. 358–369.MATHMathSciNetGoogle Scholar
  13. [13]
    Acan, A., Altincay, H., Tekol, Y., Unveren, A. (2003) A genetic algorithm with multiple crossover operators for optimal frequency assignment problem, Proceedings of IEEE Congress on Evolutionary Computation-CEC 2003, Canberra, Australia, pp. 256–263.Google Scholar
  14. [14]
    Battiti, R. (2001) A randomized saturation degree heuristic for channel assignment in cellular radio networks, IEEE Trans. On Vehicular Technology, Vol. 50, No.2, pp. 364–374.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag/Wien 2005

Authors and Affiliations

  • Adnan Acan
    • 1
  • Akin Günay
    • 1
  1. 1.Computer Engineering DepartmentEastern Mediterranean UniversityGazimağusa, T.R.N.C.Turkey

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