A Two Step Hybrid Optimization Procedure for the Design of Optimal Water Distribution Networks

  • Eric S. Fraga
  • Lazaros G. Papageorgiou
Part of the Optimization and Its Applications book series (SOIA, volume 4)


The design of a water distribution network [AS77] involves identifying the optimal pipe network, the head pressures of the individual supply and demand nodes, and the flows between the nodes, including both the amount and the direction of flow. The objective is to find the minimum cost network which meets the demands specified. Despite the objective function often being simple, consisting of a linear combination of pipe diameters and lengths, the water distribution network design problem poses challenges for optimization tools due to the tight nonlinear constraints imposed by the modelling of the relationship between node heads, water flow in a pipe, and the pipe diameter.


Genetic Algorithm Span Tree Pipe Diameter Interval Arithmetic Node Head 
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.


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  1. [AF96]
    Avis, D., Fukuda, K.: Reverse search for enumeration. Discrete Applied Mathematics, 65, 21–46 (1996)zbMATHCrossRefMathSciNetGoogle Scholar
  2. [AS77]
    Alperovits, E., Shamir, U.: Design of optimal water distribution systems. Water Resource Research, 13(6), 885–900 (1977)CrossRefGoogle Scholar
  3. [CMM98]
    Czyzyk, J., Mesnier, M.P., Moré, J.J.: The NEOS server. IEEE Computing in Science and Engineering, 5(3), 68–75 (1998)CrossRefGoogle Scholar
  4. [CS99]
    Cunha, M.C., Sousa, J.: Water distribution network design optimization: Simulated annealing approach. Journal of Water Resources Planning and Management, 125(4), 215–221 (1999)CrossRefGoogle Scholar
  5. [Deb00]
    Deb, K.: An efficient constraint handling method for genetic algorithms. Comput. Methods Appl. Mech. Engng., 186, 311–338 (2000)zbMATHCrossRefGoogle Scholar
  6. [FPS03]
    Fraga, E.S., Lazaros, G.P, Sharma, R.: Discrete model and visualization interface for water distribution network design. In: Kraslawski, A., Turunen, I. (eds) European Symposium on Computer Aided Process Engineering — 13. Vol. 14 of Computer-Aided Chemical Engineering, pp. 119–124. Elsevier Science B.V., Amsterdam (2003)Google Scholar
  7. [FSBH00]
    Fraga, E.S., Steffens, M.A., Bogle, I.D.L., Hind, A.K.: An object oriented framework for process synthesis and simulation. In: Malone, M.F., Trainham, J.A., Carnahan, B. (eds) Foundations of Computer-Aided Process Design. Vol. 96 of AIChE Symposium Series, pp. 446–449 (2000)Google Scholar
  8. [GGK99]
    Gupta, I., Gupta, A., Khanna, P.: Genetic algorithm for optimization of water distribution systems. Environmental Modelling & Software, 14, 437–446 (1999)CrossRefGoogle Scholar
  9. [GM85]
    Goulter, I.C., Morgan, D.R.: An integrated approach to the layout and design of water distribution networks. Civil Engineering Systems, 2, 104–113 (1985)Google Scholar
  10. [Gol89]
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley (1989)Google Scholar
  11. [MSZ+03]
    Maier, H.R., Simpson, A.R., Zecchin, A.C., Foong, W.K., Phang, K.Y, Seah, H.Y, Tan, C.L.: Ant colony optimization for design of water distribution systems. Journal of Water Resources Planning and Management, 129(3), 200–209 (2003)CrossRefGoogle Scholar
  12. [Ron95]
    Ronald, S.: Preventing diversity loss in a routing genetic algorithm with hash tagging. Complexity International, 2 (1995) Scholar
  13. [Smi02]
    Smith, J.E.: Genetic algorithms. In: Pardalos, P.M., Romeijn, H.E. (eds) Handbook of Global Optimization Volume 2. Nonconvex optimization and its applications, pp. 275–362. Kluwer Academic Publishers (2002)Google Scholar
  14. [STU97]
    Shioura, A., Tamura, A., Uno, T.: An optimal algorithm for scanning all spanning trees of undirected graphs. SIAM J. Comput., 26(3), 678–692 (1997)zbMATHCrossRefMathSciNetGoogle Scholar
  15. [SW97]
    Savic, D.A., Walters, G.A.: Genetic algorithms for least-cost design of water distribution networks. Journal of Water Resources Planning and Management, 123(2), 67–77 (1997)CrossRefGoogle Scholar
  16. [WS02]
    Wu, Z.Y., Simpson, A.R.: A self-adaptive boundary search genetic algorithm and its application to water distribution systems. Journal of Hydraulic Research, 40(2), 191–203 (2002)CrossRefGoogle Scholar
  17. [Zhu03]
    Zhu, K.Q.: A diversity-controlling adaptive genetic algorithm for the vehicle routing problem with time windows. In: Proceedings-15th International conference on tools with artificial intelligence (ITCAI-2003). number 15, pp. 176–183, Sacramento, California, November 2003. IEEE Computer Society (2003)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Eric S. Fraga
    • 1
  • Lazaros G. Papageorgiou
    • 1
  1. 1.Centre for Process Systems Engineering, Department of Chemical EngineeringUCL (University College London)UK

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