Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/35884
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Type: Conference paper
Title: Improving metamodel-based optimization of water distribution systems with local search
Author: Broad, D.
Dandy, G.
Maier, H.
Nixon, J.
Citation: IEEE Congress on Evolutionary Computation, 16-21 July, 2006:pp.710-717
Publisher: IEEE
Publisher Place: CDROM
Issue Date: 2006
Series/Report no.: IEEE Congress on Evolutionary Computation
ISBN: 0780394879
9780780394872
Conference Name: IEEE Congress on Evolutionary Computation (2006 : Vancouver, B.C.)
Abstract: Metamodels 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).
Description: Copyright 2006 IEEE
RMID: 0020062796
DOI: 10.1109/CEC.2006.1688381
Appears in Collections:Civil and Environmental Engineering publications
Environment Institute publications

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