Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/88990
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dc.contributor.authorZheng, F.en
dc.contributor.authorSimpson, A.en
dc.contributor.authorZecchin, A.en
dc.date.issued2014en
dc.identifier.citationWater Resources Research, 2014; 50(5):3650-3671en
dc.identifier.issn0043-1397en
dc.identifier.issn1944-7973en
dc.identifier.urihttp://hdl.handle.net/2440/88990-
dc.description.abstractAn 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.en
dc.description.statementofresponsibilityFeifei Zheng, Angus R. Simpson and Aaron C. Zecchinen
dc.language.isoenen
dc.publisherAmerican Geophysical Unionen
dc.rights© 2014 American Geophysical Union. All Rights Reserved.en
dc.subjectgraph decomposition; nonlinear programming; multiobjective differential evolution; water distribution systems; multiobjective optimizationen
dc.titleAn efficient hybrid approach for multiobjective optimization of water distribution systemsen
dc.typeJournal articleen
dc.identifier.rmid0030007823en
dc.identifier.doi10.1002/2013WR014143en
dc.identifier.pubid71537-
pubs.library.collectionCivil and Environmental Engineering publicationsen
pubs.library.teamDS02en
pubs.verification-statusVerifieden
pubs.publication-statusPublisheden
dc.identifier.orcidSimpson, A. [0000-0003-1633-0111]en
dc.identifier.orcidZecchin, A. [0000-0001-8908-7023]en
Appears in Collections:Civil and Environmental Engineering publications

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