Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/88990
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Type: Journal article
Title: An efficient hybrid approach for multiobjective optimization of water distribution systems
Author: Zheng, F.
Simpson, A.
Zecchin, A.
Citation: Water Resources Research, 2014; 50(5):3650-3671
Publisher: American Geophysical Union
Issue Date: 2014
ISSN: 0043-1397
1944-7973
Statement of
Responsibility: 
Feifei Zheng, Angus R. Simpson and Aaron C. Zecchin
Abstract: An efficient hybrid approach for the design of water distribution systems (WDSs) with multiple objectives is described in this paper. The objectives are the minimization of the network cost and maximization of the network resilience. A self-adaptive multiobjective differential evolution (SAMODE) algorithm has been developed, in which control parameters are automatically adapted by means of evolution instead of the presetting of fine-tuned parameter values. In the proposed method, a graph algorithm is first used to decompose a looped WDS into a shortest-distance tree (T) or forest, and chords (Ω). The original two-objective optimization problem is then approximated by a series of single-objective optimization problems of the T to be solved by nonlinear programming (NLP), thereby providing an approximate Pareto optimal front for the original whole network. Finally, the solutions at the approximate front are used to seed the SAMODE algorithm to find an improved front for the original entire network. The proposed approach is compared with two other conventional full-search optimization methods (the SAMODE algorithm and the NSGA-II) that seed the initial population with purely random solutions based on three case studies: a benchmark network and two real-world networks with multiple demand loading cases. Results show that (i) the proposed NLP-SAMODE method consistently generates better-quality Pareto fronts than the full-search methods with significantly improved efficiency; and (ii) the proposed SAMODE algorithm (no parameter tuning) exhibits better performance than the NSGA-II with calibrated parameter values in efficiently offering optimal fronts.
Keywords: graph decomposition; nonlinear programming; multiobjective differential evolution; water distribution systems; multiobjective optimization
Rights: © 2014 American Geophysical Union. All Rights Reserved.
RMID: 0030007823
DOI: 10.1002/2013WR014143
Appears in Collections:Civil and Environmental Engineering publications

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