Memetic Simulated Annealing for the GPS Surveying Problem

  • Stefka Fidanova
  • Enrique Alba
  • Guillermo Molina
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5434)


In designing Global Positioning System (GPS) surveying network, a given set of earth points must be observed consecutively (schedule). The cost of the schedule is the sum of the time needed to go from one point to another. The problem is to search for the best order in which this observation is executed. Minimizing the cost of this schedule is the goal of this work. Solving the problem for large networks to optimality requires impractical computational times. In this paper, several Simulated Annealing (SA) algorithms are developed to provide near-optimal solutions for large networks with bounded computational effort.


Global Position System Local Search GLObal Navigation Satellite System Test Problem Global Position System 
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.
    Arts, E., Van Loarhoven, P.: Statistical Cooling: A General Approach to Combinatorial Optimization Problems. Phillips Journal of Research 40, 193–226 (1985)MathSciNetGoogle Scholar
  2. 2.
    Chen, T.-C., Chang, Y.-W.: Modern Floorplaning Based on B* Fast Simulated Annealing. IEEE Trans. on Computer Aided Design 25(4), 637–650 (2006)CrossRefGoogle Scholar
  3. 3.
    Dare, P.J., Saleh, H.A.: GPS Network Design:Logistics Solution Using Optimal and Near-Optimal Methods. J. of Geodesy 74, 467–478 (2000)CrossRefzbMATHGoogle Scholar
  4. 4.
    Dowsland, K., Thomson, J.: Variants of Simulated Annealing for the Examination Timetabling Problem. Annals of OR 63, 105–128 (1996)CrossRefzbMATHGoogle Scholar
  5. 5.
    Fidanova, S.: An Heuristic Method for GPS Surveying Problem. In: Shi, Y., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds.) ICCS 2007. LNCS, vol. 4490, pp. 1084–1090. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  6. 6.
    Hart, W.E., Krasnogor, N., Smith, J.E. (eds.): Recent Advances in Memetic Algorithms. Studies in Fuzziness and Soft Computing, vol. 166 (2005)Google Scholar
  7. 7.
    Kirkpatrick, S., Gellat, C.D., Vecchi, P.M.: Optimization by Simulated Annealing. Science 220, 671–680 (1983)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Metropolis, N., Rosenbluth, A., Rosenbluth, M., Teller, A., Teller, E.: Equation of State Calculations by Fast Computing Machines. J. of Chem Phys. 21(6), 1087–1092 (1953)CrossRefGoogle Scholar
  9. 9.
    Rene, V.V.: Applied Simulated Annealing. Springer, Berlin (1993)zbMATHGoogle Scholar
  10. 10.
    Saleh, H.A., Dare, P.: Effective Heuristics for the GPS Survey Network of Malta: Simulated Annealing and Tabu Search Techniques. Journal of Heuristics 7(6), 533–549 (2001)CrossRefzbMATHGoogle Scholar
  11. 11.
    Schaffer, A.A., Yannakakis, M.: Simple Local Search Problems that are Hard to Solve. Society for Industrial Applied Mathematics Journal on Computing 20, 56–87 (1991)MathSciNetzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Stefka Fidanova
    • 1
  • Enrique Alba
    • 2
  • Guillermo Molina
    • 2
  1. 1.Bulgarian Academy of SciencesIPPSofiaBulgaria
  2. 2.E.T.S.I. Informática, Grupo GISUM (NEO)Universidad de MálagaMálagaEspaña

Personalised recommendations