Table of Contents
Journal of Fluids
Volume 2014 (2014), Article ID 979706, 12 pages
Research Article

Hydraulic Analysis of Water Distribution Network Using Shuffled Complex Evolution

1Civil Engineering Department, University of Torbat-e-Heydarieh, Torbat-e-Heydarieh, Iran
2Civil Engineering Department, Ferdowsi University of Mashhad, Mashhad, Iran

Received 23 September 2013; Revised 16 November 2013; Accepted 1 December 2013; Published 16 January 2014

Academic Editor: Prabir Daripa

Copyright © 2014 Naser Moosavian and Mohammad Reza Jaefarzadeh. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Hydraulic analysis of water distribution networks is an important problem in civil engineering. A widely used approach in steady-state analysis of water distribution networks is the global gradient algorithm (GGA). However, when the GGA is applied to solve these networks, zero flows cause a computation failure. On the other hand, there are different mathematical formulations for hydraulic analysis under pressure-driven demand and leakage simulation. This paper introduces an optimization model for the hydraulic analysis of water distribution networks using a metaheuristic method called shuffled complex evolution (SCE) algorithm. In this method, applying if-then rules in the optimization model is a simple way in handling pressure-driven demand and leakage simulation, and there is no need for an initial solution vector which must be chosen carefully in many other procedures if numerical convergence is to be achieved. The overall results indicate that the proposed method has the capability of handling various pipe networks problems without changing in model or mathematical formulation. Application of SCE in optimization model can lead to accurate solutions in pipes with zero flows. Finally, it can be concluded that the proposed method is a suitable alternative optimizer challenging other methods especially in terms of accuracy.