jHawanet: An Open-Source Project for the Implementation and Assessment of Multi-Objective Evolutionary Algorithms on Water Distribution Networks

• Published in: Water (Basel) Vol. 11; no. 10; p. 2018
• Main Authors: Gutiérrez-Bahamondes, Jimmy H., Salgueiro, Yamisleydi, Silva-Rubio, Sergio A., Alsina, Marco A., Mora-Meliá, Daniel, Fuertes-Miquel, Vicente S.
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2019
• Subjects: Algorithms; Applications software; case studies; Chile; Dynamic link libraries; energy requirements; Engineering research; Genetic algorithms; Hydraulics; Java; Maintenance costs; Network management systems; Optimization techniques; Performance evaluation; Variables; water conservation; water distribution; Water quality; water resources; Water-supply; Chile
• ISSN: 2073-4441; 2073-4441
• DOI: 10.3390/w11102018

Efficient design and management of water distribution networks is critical for conservation of water resources and minimization of both energy requirements and maintenance costs. Several computational routines have been proposed for the optimization of operational parameters that govern such networks. In particular, multi-objective evolutionary algorithms have proven to be useful both properly describing a network and optimizing its performance. Despite these computational advances, practical implementation of multi-objective optimization algorithms for water networks is an abstruse subject for researchers and engineers, particularly since efficient coupling between multi-objective algorithms and the hydraulic network model is required. Further, even if the coupling is successfully implemented, selecting the proper set of multi-objective algorithms for a given network, and addressing the quality of the obtained results (i.e., the approximate Pareto frontier) introduces additional complexities that further hinder the practical application of these algorithms. Here, we present an open-source project that couples the EPANET hydraulic network model with the jMetal framework for multi-objective optimization, allowing flexible implementation and comparison of different metaheuristic optimization algorithms through statistical quality assessment. Advantages of this project are discussed by comparing the performance of different multi-objective algorithms (i.e., NSGA-II, SPEA2, SMPSO) on case study water pump networks available in the literature.