Improving Metamodel-based Optimization of Water Distribution Systems with Local Search
Broad, D.R.
Dandy, G.C.
Maier, H.R.
Nixon, J.B.
Univ. of Adelaide, Adelaide;
This paper appears in: Evolutionary Computation, 2006. CEC 2006. IEEE Congress on
Publication Date: 0-0 0
On page(s): 710-717
Location: Vancouver, BC,
ISBN: 0-7803-9487-9
INSPEC Accession Number: 9723505
Digital Object Identifier: 10.1109/CEC.2006.1688381
Current Version Published: 2006-09-11
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).
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