University of Adelaide Library

Adelaide Research and Scholarship : Schools and Disciplines : School of Civil, Environmental and Mining Engineering : Civil and Environmental Engineering publications

Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/74401

Type: Conference paper
Title: Optimal rehabilitation for large water distribution systems using genetic algorithms
Author: Zheng, F.
Simpson, A.
Zecchin, A.
Citation: Proceedings of the 11th International Conference on Computing and Control for the Water Industry: Urban Water Management - Challenges and Opportunities, held at the the University of Exeter, United Kingdom, 5-7 September, 2011 / D. Savic, Z. Kapelean and D. Butler (eds.): pp.1-11
Issue Date: 2011
ISBN: 0953914089
Cover art
Conference Name: International Conference on Computing and Control for the Water Industry (11th : 2011 : Exeter, United Kingdom)
Statement of
Responsibility: 
Feifei Zheng, Angus R. Simpson and Aaron C. Zecchin
Abstract: Finding the optimal rehabilitation strategy for upgrading the performance of existing water networks to meet an increasing water supply demand is an active research field. Genetic algorithms have been employed to optimise the rehabilitation of water distribution systems (WDSs) for the last two decades. However, there is a lack of methods for assessing the search performance of genetic algorithms applied to large optimisation problems as the global optimal solution for large problems is typically unknown. This paper aims to investigate the search ability of genetic algorithms applied to a number of case studies with the number of decision variables ranging from 21 to 1050 pipes. The performance assessment of genetic algorithms is made by comparing the optimal solution found by genetic algorithms and the estimated global optimal solution for each case study. It can be seen that, from the results obtained, genetic algorithms exhibit a high overall search performance when the number of decision variables is less than 100. However, the performance of genetic algorithms deteriorates when dealing with relatively larger WDS case studies with number of decision variables more than 200.
Rights: Copyright © 2011 CWS
RMID: 0020118846
Description (link): http://events.exeter.ac.uk/ccwi2011/
Appears in Collections:Civil and Environmental Engineering publications
Environment Institute publications

There are no files associated with this item.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

 

© 2008 The University of Adelaide
library@adelaide.edu.au
CRICOS Provider Number 00123M
Service Charter | Copyright | Privacy | Disclaimer