Abstract
Language | English |
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Number of pages | 16 |
Journal | Water Resources Management |
Early online date | 13 May 2018 |
DOIs | |
Publication status | E-pub ahead of print - 13 May 2018 |
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Keywords
- dynamic solution-space reduction
- maximum entropy formalism
- reliability-based design
- water distribution network
- self-adaptive boundary search
- failure tolerance and resilience
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Self-adaptive solution-space reduction algorithm for multi-objective evolutionary design optimization of water distribution networks. / Tanyimboh, Tiku T.; Czajkowska, Anna.
In: Water Resources Management, 13.05.2018.Research output: Contribution to journal › Article
TY - JOUR
T1 - Self-adaptive solution-space reduction algorithm for multi-objective evolutionary design optimization of water distribution networks
AU - Tanyimboh, Tiku T.
AU - Czajkowska, Anna
PY - 2018/5/13
Y1 - 2018/5/13
N2 - An effective way to improve the computational efficiency of evolutionary algorithms is to make the solution space of the optimization problem under consideration smaller. A new reliability-based algorithm that does this was developed for water distribution networks. The objectives considered in the formulation of the optimization problem were minimization of the initial construction cost and maximization of the flow entropy as a resilience surrogate. After achieving feasible solutions, the active solution space of the optimization problem was re-set for each pipe in each generation until the end of the optimization. The algorithm re-set the active solution space by reducing the number of pipe diameter options for each pipe, based on the most likely flow distribution. The main components of the methodology included an optimizer, a hydraulic simulator and an algorithm that calculates the flow entropy for any given network configuration. The methodology developed is generic and self-adaptive, and prior setting of the reduced solution space is not required. A benchmark network in the literature was investigated, and the results showed that the algorithm improved the computational efficiency and quality of the solutions achieved by a considerable margin.
AB - An effective way to improve the computational efficiency of evolutionary algorithms is to make the solution space of the optimization problem under consideration smaller. A new reliability-based algorithm that does this was developed for water distribution networks. The objectives considered in the formulation of the optimization problem were minimization of the initial construction cost and maximization of the flow entropy as a resilience surrogate. After achieving feasible solutions, the active solution space of the optimization problem was re-set for each pipe in each generation until the end of the optimization. The algorithm re-set the active solution space by reducing the number of pipe diameter options for each pipe, based on the most likely flow distribution. The main components of the methodology included an optimizer, a hydraulic simulator and an algorithm that calculates the flow entropy for any given network configuration. The methodology developed is generic and self-adaptive, and prior setting of the reduced solution space is not required. A benchmark network in the literature was investigated, and the results showed that the algorithm improved the computational efficiency and quality of the solutions achieved by a considerable margin.
KW - dynamic solution-space reduction
KW - maximum entropy formalism
KW - reliability-based design
KW - water distribution network
KW - self-adaptive boundary search
KW - failure tolerance and resilience
UR - https://link.springer.com/journal/volumesAndIssues/11269
U2 - 10.1007/s11269-018-1994-5
DO - 10.1007/s11269-018-1994-5
M3 - Article
JO - Water Resources Management
T2 - Water Resources Management
JF - Water Resources Management
SN - 0920-4741
ER -