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Abstract
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 reliabilitybased 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 reset for each pipe in each generation until the end of the optimization. The algorithm reset 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 selfadaptive, 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.
Original language  English 

Number of pages  16 
Journal  Water Resources Management 
Early online date  13 May 2018 
DOIs  
Publication status  Epub ahead of print  13 May 2018 
Keywords
 dynamic solutionspace reduction
 maximum entropy formalism
 reliabilitybased design
 water distribution network
 selfadaptive boundary search
 failure tolerance and resilience
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Dive into the research topics of 'Selfadaptive solutionspace reduction algorithm for multiobjective evolutionary design optimization of water distribution networks'. Together they form a unique fingerprint.Projects
 1 Finished

Penaltyfree feasibility boundaryconvergent multiobjective evolutionary approach for water distribution  Czajkowska, Anna
Tanyimboh, T., Pytharouli, S. & Czajkowska, A.
EPSRC (Engineering and Physical Sciences Research Council)
1/10/09 → 6/06/16
Project: Research  Internally Allocated