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https://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.) |
Editor: | Yen, G. |
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 |
DOI: | 10.1109/CEC.2006.1688381 |
Published version: | http://dx.doi.org/10.1109/cec.2006.1688381 |
Appears in Collections: | Aurora harvest Civil and Environmental Engineering publications Environment Institute publications |
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