Optimizing the Location Areas Planning in the SUMATRA Network with an Adaptation of the SPEA2 Algorithm

  • Víctor Berrocal-Plaza
  • Miguel A. Vega-Rodríguez
  • Juan M. Sánchez-Pérez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8111)


This paper presents our adaptation of the Strength Pareto Evolutionary Algorithm 2 (SPEA2, a Multi-Objective Evolutionary Algorithm) to optimize the Location Areas Planning Problem. Location Areas is a strategy widely used to manage one of the most important issues of the Public Land Mobile Networks: the mobile location management. In contrast to previous works, we propose a multi-objective approach with the goal of avoiding the drawbacks associated with the linear aggregation of the objective functions. The main advantage of a multi-objective approach is that this kind of algorithm provides a wide range of solutions among which the network operator could select the solution that best adjusts to the network real state at each moment. Furthermore, in order to obtain realistic results, we apply our proposal to the SUMATRA network, a test network that stores real-time information of the users’ mobile activity in the San Francisco Bay (USA). Experimental results show that our proposal outperforms the results obtained in other works and, at the same time, it achieves a great spread of solutions.


Location Areas Planning Problem Mobile Location Management Multi-objective Optimization Stanford University Mobile Activity Traces Strength Pareto Evolutionary Algorithm 2 


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  1. 1.
    Agrawal, D., Zeng, Q.: Introduction to Wireless and Mobile Systems. Cengage Learning (2010)Google Scholar
  2. 2.
    Kyamakya, K., Jobmann, K.: Location management in cellular networks: classification of the most important paradigms, realistic simulation framework, and relative performance analysis. IEEE Transactions on Vehicular Technology 54(2), 687–708 (2005)CrossRefGoogle Scholar
  3. 3.
    Krishnamachari, B., Gau, R.H., Wicker, S.B., Haas, Z.J.: Optimal sequential paging in cellular wireless networks. Wirel. Netw. 10(2), 121–131 (2004)CrossRefGoogle Scholar
  4. 4.
    Gondim, P.: Genetic algorithms and the location area partitioning problem in cellular networks. In: Procedings of the IEEE 46th Vehicular Technology Conference on Mobile Technology for the Human Race, vol. 3, pp. 1835–1838 (1996)Google Scholar
  5. 5.
    Demestichas, P., Georgantas, N., Tzifa, E., Demesticha, V., Striki, M., Kilanioti, M., Theologou, M.E.: Computationally efficient algorithms for location area planning in future cellular systems. Computer Communications 23(13), 1263–1280 (2000)CrossRefGoogle Scholar
  6. 6.
    Taheri, J., Zomaya, A.Y.: The use of a hopfield neural network in solving the mobility management problem. In: Proceedings of the IEEE/ACS International Conference on Pervasive Services, pp. 141–150 (2004)Google Scholar
  7. 7.
    Taheri, J., Zomaya, A.Y.: A genetic algorithm for finding optimal location area configurations for mobility management. In: The IEEE Conference on Local Computer Networks 30th Anniversary, pp. 568–577 (2005)Google Scholar
  8. 8.
    Taheri, J., Zomaya, A.Y.: A simulated annealing approach for mobile location management. In: Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium, p. 194 (2005)Google Scholar
  9. 9.
    Taheri, J., Zomaya, A.Y.: A combined genetic-neural algorithm for mobility management. J. Math. Model. Algorithms, 481–507 (2007)Google Scholar
  10. 10.
    Subrata, R., Zomaya, A.Y.: Dynamic location management for mobile computing. Telecommunication Systems 22(1-4), 169–187 (2003)CrossRefGoogle Scholar
  11. 11.
    Stanford University Mobile Activity TRAces (SUMATRA), (accessed in 2013)
  12. 12.
    Almeida-Luz, S., Vega-Rodríguez, M.A., Gómez-Pulido, J.A., Sánchez-Pérez, J.M.: Applying differential evolution to a realistic location area problem using sumatra. In: Proceedings of the Second International Conference on Advanced Engineering Computing and Applications in Sciences, ADVCOMP 2008, pp. 170–175. IEEE Computer Society, Washington, DC (2008)CrossRefGoogle Scholar
  13. 13.
    Almeida-Luz, S.M., Vega-Rodríguez, M.A., Gómez-Pulido, J.A., Sánchez-Pérez, J.M.: Solving a realistic location area problem using sumatra networks with the scatter search algorithm. In: Proceedings of the Ninth International Conference on Intelligent Systems Design and Applications, ISDA 2009, pp. 689–694. IEEE Computer Society, Washington, DC (2009)CrossRefGoogle Scholar
  14. 14.
    Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the strength pareto evolutionary algorithm for multiobjective optimization. In: Giannakoglou, K.C., Tsahalis, D.T., Périaux, J., Papailiou, K.D., Fogarty, T. (eds.) Evolutionary Methods for Design Optimization and Control with Applications to Industrial Problems, Athens, Greece, pp. 95–100. International Center for Numerical Methods in Engineering (2001)Google Scholar
  15. 15.
    Coello, C.A.C., Lamont, G.B., Veldhuizen, D.A.V.: Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation). Springer-Verlag New York, Inc., Secaucus (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Víctor Berrocal-Plaza
    • 1
  • Miguel A. Vega-Rodríguez
    • 1
  • Juan M. Sánchez-Pérez
    • 1
  1. 1.Dept. Technologies of Computers & CommunicationsUniversity of Extremadura, Escuela PolitécnicaCáceresSpain

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