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dc.contributor.authorBroad, D.-
dc.contributor.authorDandy, G.-
dc.contributor.authorMaier, H.-
dc.contributor.authorNixon, J.-
dc.contributor.editorYen, G.-
dc.identifier.citationIEEE Congress on Evolutionary Computation, 16-21 July, 2006:pp.710-717-
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.relation.ispartofseriesIEEE Congress on Evolutionary Computation-
dc.titleImproving metamodel-based optimization of water distribution systems with local search-
dc.typeConference paper-
dc.contributor.conferenceIEEE Congress on Evolutionary Computation (2006 : Vancouver, B.C.)-
dc.identifier.orcidDandy, G. [0000-0001-5846-7365]-
dc.identifier.orcidMaier, H. [0000-0002-0277-6887]-
Appears in Collections:Aurora harvest
Civil and Environmental Engineering publications
Environment Institute publications

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