Improving metamodel-based optimization of water distribution systems with local search

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dc.contributor.authorBroad, D.
dc.contributor.authorDandy, G.
dc.contributor.authorMaier, H.
dc.contributor.authorNixon, J.
dc.contributor.conferenceIEEE Congress on Evolutionary Computation (2006 : Vancouver, B.C.)
dc.contributor.editorYen, G.
dc.date.issued2006
dc.descriptionCopyright 2006 IEEE
dc.description.abstractMetamodels can be used to aid in improving the efficiency of computationally expensive optimization algorithms in a variety of applications, including water distribution system (WDS) design and operation. Genetic Algorithm (GA)-based optimization of WDSs is very computationally expensive to optimize a system in a practical amount of time for real-sized problems. A metamodel, of which Artificial Neural Networks (ANNs) are an example, is a model of a complex simulation model. It can be used in place of the simulation model where repeated use is necessary, such as when carrying out GA optimization. To complement the ANN-GA, six local search algorithms have been developed or applied in this research, with the aim of improving the performance of metamodel-based optimization of WDSs. All algorithms performed well, however, using computational intensity as a criterion with which to evaluate results, the best local search algorithms were Sequential Downward Mutation (SDM) and Maximum Savings Downward Mutation (MSDM).
dc.identifier.citationIEEE Congress on Evolutionary Computation, 16-21 July, 2006:pp.710-717
dc.identifier.doi10.1109/CEC.2006.1688381
dc.identifier.isbn0780394879
dc.identifier.isbn978-0-7803-9487-2
dc.identifier.orcidDandy, G. [0000-0001-5846-7365]
dc.identifier.orcidMaier, H. [0000-0002-0277-6887]
dc.identifier.urihttp://hdl.handle.net/2440/35884
dc.language.isoen
dc.publisherIEEE
dc.publisher.placeCDROM
dc.relation.ispartofseriesIEEE Congress on Evolutionary Computation
dc.source.urihttps://doi.org/10.1109/cec.2006.1688381
dc.titleImproving metamodel-based optimization of water distribution systems with local search
dc.typeConference paper
pubs.publication-statusPublished

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