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Discussion papers | Copyright
https://doi.org/10.5194/dwes-2018-21
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Technical note 16 Aug 2018

Technical note | 16 Aug 2018

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Drinking Water Engineering and Science (DWES).

Technical note: Problem specific variators in a genetic algorithm for the optimization of drinking water networks

Karel van Laarhoven, Ina Vertommen, and Peter van Thienen Karel van Laarhoven et al.
  • KWR Watercycle Research Institute, Nieuwegein, 3430 BB, the Netherlands

Abstract. Genetic algorithms can be a powerful tool for the automated design of optimal drinking water distribution networks. Fast convergence of such algorithms is a crucial factor for successful practical implementation at the drinking water utility level. In this technical note, we therefore investigate the performance of a suite of genetic variators that was tailored to the optimisation of a least-cost network design. Different combinations of the variators are tested in terms of convergence rate and the robustness of the results during optimisation of the real world drinking water distribution network of Sittard, the Netherlands. The variator configurations that reproducibly reach the furthest convergence after 105 function evaluations are reported. In the future these may aid in dealing with the computational challenges of optimizing real world networks.

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Karel van Laarhoven et al.
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Short summary
This paper concerns the extension and tuning of a genetic algorithm used for the automated design of optimal drinking water distribution networks. Different settings and extensions are tested for their effect on the speed and reproducability with which the algorithm can produce good results. The fastest combinations are reported. Speed and reproducability are key conditions for drinkingwater utilities to include the use of optimization algorithms in the regular design process of mains.
This paper concerns the extension and tuning of a genetic algorithm used for the automated...
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