Abstract
The Minimum Interference Frequency Assignment Problem (MI-FAP) is an important optimization problem that arises in operational wireless networks. Solution techniques based on meta-heuristic algorithms have been shown to be successful for some test problems. However, they have not been demonstrated on the large scale problems that occur in practice, and their performance is poor in these cases. We propose a decomposed assignment technique which divides the initial problem into a number of simpler subproblems that are solved either independently or in sequence. Partial subproblems solutions are recomposed into a solution of the original problem. Our results, show that the decomposed assignment approach proposed can improve the outcomes, both in terms of solution quality and runtime. A number of partitioning methods are presented and compared, such as clique detection; partitioning based on sequential orderings; and novel applications of existing graph partitioning and clustering methods adapted for this problem.
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References
FAP web - A website about Frequency Assignment Problems (2007), http://fap.zib.de/ (accessed on June 1, 2007)
Allen, S.M., Dunkin, N., Hurley, S., Smith, D.: Frequency assignment problems: benchmarks and lower bounds. University of Glamorgan, UK (1998)
Brandes, U., Gaertler, M., Wagner, D.: Experiments on Graph Clustering Algorithms. In: Proc. of the 11th Annual European Symposium on Algorithms, Budapest (2003)
Colombo, G.: A Genetic Algorithm for frequency assignment with problem decomposition. International Journal of Mobile Network Design and Innovation 1-2, 102–112 (2006)
Colombo, G., Allen, S.M.: Problem decomposition for Minimum Interference Frequency Assignment. In: Proc. of the IEEE Congress in and Evolutionary Computation, Singapore (2007)
Colombo, G., Mumford, C.L.: Comparing Algorithms, Representations and Operators for the Multi–objective Knapsack Problem. In: Proceedings of the 2005 IEEE Congress on Evolutionary Computation (CEC 2005), Edinburgh, Scotland, pp. 1268–1275 (2005)
Correia, L.M. (ed.): Wireless Flexible Personalised Communications. Wiley, Chichester (2001)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA–II. IEE Trans. on Evolutionary Computation 6, 182–197 (2002)
Eisenblatter, A.: Frequency Assignment in GSM Networks: Models, Heuristics, and Lower Bounds. PhD thesis, Technische Universitat Berlin, Berlin, Germany (2001)
Gamst, A.: Some lower bounds for a class of frequency assignment problems. IEEE Transactions on Vehicular Technology 35, 8–14 (1986)
Grace, D., Burr, A.G., Tozer, T.C.: Comparison of Different Distributed Channel Assignment Algorithms for UFDMA. In: 2nd IEEE International Conference on Personal, Mobile and Spread Spectrum Communications, pp. 38–41 (1996)
Hale, W.K.: Frequency assignment: Theory and applications. Proc. IEEE 38, 1497–1514 (1980)
Hale, W.K.: New spectrum management tools. In: Proc. Ieee International Symposium on Electromagnetic Compatibility, pp. 47–53 (1981)
Hurley, S., Smith, D.: Meta-Heuristics and channel assignment. In: Hurley, S., Leese, R. (eds.) Methods and algorithms for radio channel assignment. Oxford University Press, Oxford (2002)
Hurley, S., Smith, D., Thiel, S.U.: Fasoft: a system for discrete channel frequency assignment. Radio Science 32(5), 1921–1939 (1997)
Karaoglu, N., Manderick, B.: FAPSTER - a genetic algorithm for frequency assignment problem. In: Proc. of the 2005 Genetic and Evolutionary Computation Conference, Washington D.C., USA (2005)
Koster, A.M.C.A., van Hoesel, C.P.M., Kolen, A.W.J.: Solving partial constraint satisfaction problems with tree decomposition. Networks 40(3), 170–180 (2002)
Mannino, C., Sassano, A.: An enumerative algorithm for the frequency assignment problem. Discrete Applied Mathematics 129(1), 155–169 (2003)
Mannino, C., Oriolo, G., Ricci, F.: Solving Stability Problems on a Superclass of Interval Graphs. T.R. n. 511, Vito Volterra (2002)
Montemanni, R., Moon, J.N., Smith, D.H.: An improved Tabu Search algorithm for the Fixed-Spectrum Frequency-Assignment problem. IEE Transactions on Vehicular technology 52(3), 891–901 (2003)
Pardalos, P., Rappe, J., Resende, M.: An exact parallel algorithm for the maximum clique problem. In: De Leone, P.P.R., Murl’i, A., Toraldo, G. (eds.) High Performance Algorithms and Software in Nonlinear Optimization. Kluwer, Dordrecht (1998)
van Dongen, S.: A cluster algorithm for graphs, Technical Report INS-R0010, National Research Institute for Mathematics and Computer Science in the Netherlands, Amsterdam (2000)
Waharte, S., Boutaba, R.: Comparison of Distributed Frequency Assignment Algorithms for Wireless Sensor Network, Technical Report, University of Waterloo, ON, Canada
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Colombo, G., Allen, S.M. (2008). A Decomposed Approach for the Minimum Interference Frequency Assignment. In: Chen, Yp., Lim, MH. (eds) Linkage in Evolutionary Computation. Studies in Computational Intelligence, vol 157. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85068-7_16
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DOI: https://doi.org/10.1007/978-3-540-85068-7_16
Publisher Name: Springer, Berlin, Heidelberg
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