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jHawanet: an open-source project for the implementation and assessment of multi-objective evolutionary algorithms on water distribution networks

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jHawanet: an open-source project for the implementation and assessment of multi-objective evolutionary algorithms on water distribution networks

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dc.contributor.author Gutierrez-Bahamondes, Jimmy H. es_ES
dc.contributor.author Salgueiro, Yamisleydi es_ES
dc.contributor.author Silva-Rubio, Sergio A. es_ES
dc.contributor.author Alsina, Marco A. es_ES
dc.contributor.author Mora-Melia, Daniel es_ES
dc.contributor.author Fuertes-Miquel, Vicente S. es_ES
dc.date.accessioned 2020-12-23T04:31:38Z
dc.date.available 2020-12-23T04:31:38Z
dc.date.issued 2019-10 es_ES
dc.identifier.issn 2073-4441 es_ES
dc.identifier.uri http://hdl.handle.net/10251/157762
dc.description.abstract [EN] 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 es_ES
dc.description.sponsorship This research and the APC were funded by the Comision Nacional de Investigacion Cientifica y Tecnologica (Conicyt), grant number 1180660 es_ES
dc.language Inglés es_ES
dc.publisher MDPI AG es_ES
dc.relation.ispartof Water es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Optimization es_ES
dc.subject Multi-objective evolutionary algorithms es_ES
dc.subject Water distribution networks es_ES
dc.subject Hydraulic network modeling es_ES
dc.subject EPANET es_ES
dc.subject JMetal es_ES
dc.subject NSGA-II es_ES
dc.subject.classification MECANICA DE FLUIDOS es_ES
dc.title jHawanet: an open-source project for the implementation and assessment of multi-objective evolutionary algorithms on water distribution networks es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.3390/w11102018 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/CONICYT//1180660/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería Hidráulica y Medio Ambiente - Departament d'Enginyeria Hidràulica i Medi Ambient es_ES
dc.description.bibliographicCitation Gutierrez-Bahamondes, JH.; Salgueiro, Y.; Silva-Rubio, SA.; Alsina, MA.; Mora-Melia, D.; Fuertes-Miquel, VS. (2019). jHawanet: an open-source project for the implementation and assessment of multi-objective evolutionary algorithms on water distribution networks. Water. 11(10):1-17. https://doi.org/10.3390/w11102018 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.3390/w11102018 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 17 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 11 es_ES
dc.description.issue 10 es_ES
dc.relation.pasarela S\410120 es_ES
dc.contributor.funder Comisión Nacional de Investigación Científica y Tecnológica, Chile es_ES
dc.description.references Wang, Y., Hua, Z., & Wang, L. (2018). Parameter Estimation of Water Quality Models Using an Improved Multi-Objective Particle Swarm Optimization. Water, 10(1), 32. doi:10.3390/w10010032 es_ES
dc.description.references Letting, L., Hamam, Y., & Abu-Mahfouz, A. (2017). Estimation of Water Demand in Water Distribution Systems Using Particle Swarm Optimization. Water, 9(8), 593. doi:10.3390/w9080593 es_ES
dc.description.references Ngamalieu-Nengoue, U. A., Martínez-Solano, F. J., Iglesias-Rey, P. L., & Mora-Meliá, D. (2019). Multi-Objective Optimization for Urban Drainage or Sewer Networks Rehabilitation through Pipes Substitution and Storage Tanks Installation. Water, 11(5), 935. doi:10.3390/w11050935 es_ES
dc.description.references Morley, M. ., Atkinson, R. ., Savić, D. ., & Walters, G. . (2001). GAnet: genetic algorithm platform for pipe network optimisation. Advances in Engineering Software, 32(6), 467-475. doi:10.1016/s0965-9978(00)00107-1 es_ES
dc.description.references Van Thienen, P., & Vertommen, I. (2015). Gondwana: A Generic Optimization Tool for Drinking Water Distribution Systems Design and Operation. Procedia Engineering, 119, 1212-1220. doi:10.1016/j.proeng.2015.08.978 es_ES
dc.description.references Mala-Jetmarova, H., Sultanova, N., & Savic, D. (2017). Lost in optimisation of water distribution systems? A literature review of system operation. Environmental Modelling & Software, 93, 209-254. doi:10.1016/j.envsoft.2017.02.009 es_ES
dc.description.references Durillo, J. J., & Nebro, A. J. (2011). jMetal: A Java framework for multi-objective optimization. Advances in Engineering Software, 42(10), 760-771. doi:10.1016/j.advengsoft.2011.05.014 es_ES
dc.description.references Ravber, M., Mernik, M., & Črepinšek, M. (2017). The impact of Quality Indicators on the rating of Multi-objective Evolutionary Algorithms. Applied Soft Computing, 55, 265-275. doi:10.1016/j.asoc.2017.01.038 es_ES


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