DBPapers
DOI: 10.5593/SGEM2016/B31/S12.071

OPTIMAL COST DESIGN OF WATER DISTRIBUTION NETWORK USING HARMONY SEARCH AND ANT COLONY ALGORITHM

L. Vuta, G. Dumitran, V.Piraianu, C. Dragoi, A. Catalin
Wednesday 7 September 2016 by Libadmin2016

References: 16th International Multidisciplinary Scientific GeoConference SGEM 2016, www.sgem.org, SGEM2016 Conference Proceedings, ISBN 978-619-7105-61-2 / ISSN 1314-2704, June 28 - July 6, 2016, Book3 Vol. 1, 545-552 pp

ABSTRACT
Water distribution systems (WDS) are costly in terms of materials, construction, maintenance, and energy requirements. Much attention has been given to the application of optimization methods to minimize the costs associated with such infrastructure. Optimal design of large water distribution networks belongs to the nonlinear, combinatorial optimization problems. The discrete nature of the pipe diameters values and the nonlinear equations governing hydraulic regime make to be very difficult to solve such problems via classical optimization techniques. Within the last decades, many researchers have shifted the focus of WDS optimization from traditional optimization techniques based on linear and non-linear programming to the implementation of evolutionary computation namely: genetic algorithms, simulated annealing, ant colony optimization (ACOA), bee colony algorithm, harmony search (HS) and so on. All the studies emphasize the superiority of such computational methods in optimal design of water distribution networks.
This paper presents the results obtained by applying ACO and HS for optimal design of a water distribution network of a medium size city, based on hydraulic restriction. Both algorithms provided lower cost solutions (10 – 24%) than the presently used solution. Besides the differences in costs, the optimal solutions identified are characterized by shorter travel times, which can be translated into a better water quality at the consumers.

Keywords: optimal dimensioning, water distribution systems; Ant Colony Optimization Algorithm, Harmony Search Algorithm