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Article

A New Evolutionary Approach to Optimal Sensor Placement in Water Distribution Networks

1
Department of Computer Science, Systems and Communication, University of Milano-Bicocca, 20126 Milan, Italy
2
Department of Economics, Management and Statistics, University of Milano-Bicocca, 20126 Milan, Italy
*
Author to whom correspondence should be addressed.
Academic Editor: Stefano Alvisi
Water 2021, 13(12), 1625; https://doi.org/10.3390/w13121625
Received: 26 April 2021 / Revised: 4 June 2021 / Accepted: 8 June 2021 / Published: 9 June 2021
(This article belongs to the Section Urban Water Management)
The sensor placement problem is modeled as a multi-objective optimization problem with Boolean decision variables. A new multi objective evolutionary algorithm (MOEA) is proposed for approximating and analyzing the set of Pareto optimal solutions. The evaluation of the objective functions requires the execution of a hydraulic simulation model of the network. To organize the simulation results a data structure is proposed which enables the dynamic representation of a sensor placement and its fitness as a heatmap. This allows the definition of information spaces, in which the fitness of a placement can be represented as a matrix or, in probabilistic terms as a histogram. The key element in the new algorithm is this probabilistic representation which is embedded in a space endowed with a metric based on a specific notion of distance. Among several distances between probability distributions the Wasserstein (WST) distance has been selected: WST has enabled to derive new genetic operators, indicators of the quality of the Pareto set and criteria to choose among the Pareto solutions. The new algorithm has been tested on a benchmark water distribution network with two objective functions showing an improvement over NSGA-II, in particular for low generation counts, making it a good candidate for expensive black-box multi-objective optimization View Full-Text
Keywords: sensor placement; water network; multi-objective optimization; evolutionary optimization sensor placement; water network; multi-objective optimization; evolutionary optimization
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MDPI and ACS Style

Ponti, A.; Candelieri, A.; Archetti, F. A New Evolutionary Approach to Optimal Sensor Placement in Water Distribution Networks. Water 2021, 13, 1625. https://doi.org/10.3390/w13121625

AMA Style

Ponti A, Candelieri A, Archetti F. A New Evolutionary Approach to Optimal Sensor Placement in Water Distribution Networks. Water. 2021; 13(12):1625. https://doi.org/10.3390/w13121625

Chicago/Turabian Style

Ponti, Andrea, Antonio Candelieri, and Francesco Archetti. 2021. "A New Evolutionary Approach to Optimal Sensor Placement in Water Distribution Networks" Water 13, no. 12: 1625. https://doi.org/10.3390/w13121625

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