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|Title:||Improving metamodel-based optimization of water distribution systems with local search|
|Citation:||IEEE Congress on Evolutionary Computation, 16-21 July, 2006:pp.710-717|
|Series/Report no.:||IEEE Congress on Evolutionary Computation|
|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|
|Appears in Collections:||Civil and Environmental Engineering publications|
Environment Institute publications
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