A Two Step Hybrid Optimization Procedure for the Design of Optimal Water Distribution Networks
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.
KeywordsGenetic Algorithm Span Tree Pipe Diameter Interval Arithmetic Node Head
Unable to display preview. Download preview PDF.
- [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
- [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
- [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
- [Gol89]Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley (1989)Google Scholar
- [Ron95]Ronald, S.: Preventing diversity loss in a routing genetic algorithm with hash tagging. Complexity International, 2 (1995) http://www.complexity.org.au/Google Scholar
- [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
- [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