Self-Adaptive Solution-Space Reduction Algorithm for Multi-Objective Evolutionary Design Optimization of Water Distribution Networks

Tiku T. Tanyimboh () and Anna Czajkowska ()
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Tiku T. Tanyimboh: University of the Witwatersrand
Anna Czajkowska: University of Strathclyde

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2018, vol. 32, issue 10, No 6, 3337-3352

Abstract: 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 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-sets 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 include 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.

Keywords: Dynamic solution-space reduction; Maximum entropy formalism; Reliability-based design; Water distribution network; Self-adaptive boundary search; Failure tolerance and resilience (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s11269-018-1994-5

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