Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/73725
Citations
Scopus Web of Science® Altmetric
?
?
Full metadata record
DC FieldValueLanguage
dc.contributor.authorZecchin, A.en
dc.contributor.authorSimpson, A.en
dc.contributor.authorMaier, H.en
dc.contributor.authorMarchi, A.en
dc.contributor.authorNixon, J.en
dc.date.issued2012en
dc.identifier.citationWater Resources Research, 2012; 48(9):1-16en
dc.identifier.issn0043-1397en
dc.identifier.urihttp://hdl.handle.net/2440/73725-
dc.description.abstract[1] Evolutionary algorithms (EAs) have been applied successfully to many water resource problems, such as system design, management decision formulation, and model calibration. The performance of an EA with respect to a particular problem type is dependent on how effectively its internal operators balance the exploitation/exploration trade-off to iteratively find solutions of an increasing quality. For a given problem, different algorithms are observed to produce a variety of different final performances, but there have been surprisingly few investigations into characterizing how the different internal mechanisms alter the algorithm’s searching behavior, in both the objective and decision space, to arrive at this final performance. This paper presents metrics for analyzing the searching behavior of ant colony optimization algorithms, a particular type of EA, for the optimal water distribution system design problem, which is a classical NP-hard problem in civil engineering. Using the proposed metrics, behavior is characterized in terms of three different attributes: (1) the effectiveness of the search in improving its solution quality and entering into optimal or near-optimal regions of the search space, (2) the extent to which the algorithm explores as it converges to solutions, and (3) the searching behavior with respect to the feasible and infeasible regions. A range of case studies is considered, where a number of ant colony optimization variants are applied to a selection of water distribution system optimization problems. The results demonstrate the utility of the proposed metrics to give greater insight into how the internal operators affect each algorithm’s searching behavior.en
dc.description.statementofresponsibilityA.C. Zecchin, A.R. Simpson, H.R. Maier, A. Marchi and J.B. Nixonen
dc.language.isoenen
dc.publisherAmer Geophysical Unionen
dc.rights© 2012 American Geophysical Union. All Rights Reserveden
dc.titleImproved understanding of the searching behavior of ant colony optimization algorithms applied to the water distribution design problemen
dc.typeJournal articleen
dc.identifier.rmid0020122039en
dc.identifier.doi10.1029/2011WR011652en
dc.identifier.pubid23146-
pubs.library.collectionCivil and Environmental Engineering publicationsen
pubs.verification-statusVerifieden
pubs.publication-statusPublisheden
dc.identifier.orcidZecchin, A. [0000-0001-8908-7023]en
dc.identifier.orcidSimpson, A. [0000-0003-1633-0111]en
dc.identifier.orcidMaier, H. [0000-0002-0277-6887]en
Appears in Collections:Civil and Environmental Engineering publications
Environment Institute publications

Files in This Item:
File Description SizeFormat 
hdl_73725.pdfPublished version784.77 kBAdobe PDFView/Open


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