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Article

Use of Energy-Based Domain Knowledge as Feedback to Evolutionary Algorithms for the Optimization of Water Distribution Networks

1
Department of Civil Engineering, Queen’s University, Kingston, ON K7L 3N6, Canada
2
Department of Civil & Architectural Engineering & Mechanics, University of Arizona, Tucson, AZ 85721, USA
3
Water Supply and Sewer Systems Research Center (CIACUA), Universidad de los Andes, Bogotá 111711, Colombia
4
Department of Civil and Environmental Engineering, Universidad de los Andes, Bogotá 111711, Colombia
*
Author to whom correspondence should be addressed.
Water 2020, 12(11), 3101; https://doi.org/10.3390/w12113101
Received: 8 July 2020 / Revised: 18 October 2020 / Accepted: 28 October 2020 / Published: 4 November 2020
The optimization of water distribution networks (WDN) has evolved, requiring approaches that seek to reduce capital costs and maximize the reliability of the system simultaneously. Hence, several evolutionary algorithms, such as the non-dominated sorting-based multi-objective evolutionary algorithm (NSGA-II), have been widely used despite the high computational costs required to achieve an acceptable solution. Alternatively, energy-based methods have been used to reach near-optimal solutions with reduced computational requirements. This paper presents a method to combine the domain knowledge given by energy-based methods with an evolutionary algorithm, in a way that improves the convergence rate and reduces the overall computational requirements to find near-optimal Pareto fronts (PFs). This method is divided into three steps: parameters calibration, preprocessing of the optimal power use surface (OPUS) results, and periodic feedback using OPUS in NSGA II. The method was tested in four benchmark networks with different characteristics, seeking to minimize the costs of the WDN and maximizing its reliability. Then the results were compared with a generic implementation of NSGA-II, and the performance and quality of the solutions were evaluated using two metrics: hypervolume (HV) and modified inverted generational distance (IGD+). The results showed that the feedback procedure increases the efficiency of the algorithm, particularly the first time the algorithm is retrofitted. View Full-Text
Keywords: multi-objective evolutionary algorithm; NSGA-II; optimal power use surface (OPUS); hypervolume; IGD+; domain knowledge; hot start multi-objective evolutionary algorithm; NSGA-II; optimal power use surface (OPUS); hypervolume; IGD+; domain knowledge; hot start
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MDPI and ACS Style

Páez, D.; Salcedo, C.; Garzón, A.; González, M.A.; Saldarriaga, J. Use of Energy-Based Domain Knowledge as Feedback to Evolutionary Algorithms for the Optimization of Water Distribution Networks. Water 2020, 12, 3101. https://doi.org/10.3390/w12113101

AMA Style

Páez D, Salcedo C, Garzón A, González MA, Saldarriaga J. Use of Energy-Based Domain Knowledge as Feedback to Evolutionary Algorithms for the Optimization of Water Distribution Networks. Water. 2020; 12(11):3101. https://doi.org/10.3390/w12113101

Chicago/Turabian Style

Páez, Diego; Salcedo, Camilo; Garzón, Alexander; González, María A.; Saldarriaga, Juan. 2020. "Use of Energy-Based Domain Knowledge as Feedback to Evolutionary Algorithms for the Optimization of Water Distribution Networks" Water 12, no. 11: 3101. https://doi.org/10.3390/w12113101

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