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

[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 literatureThis research and the APC were funded by the Comision Nacional de Investigacion Cientifica y Tecnologica (Conicyt), grant number 1180660Gutierrez-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/w111020181171110Wang, 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/w10010032Letting, 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/w9080593Ngamalieu-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/w11050935Morley, 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)00107-1Van 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.2015.08.978Mala-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.2017.02.009Durillo, 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.2011.05.014Ravber, 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.2017.01.03