Self-adaptive solution-space reduction algorithm for multi-objective evolutionary design optimization of water distribution networks

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 -