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

Abstract

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.

Pumping Station Design in Water Distribution Networks Considering the Optimal Flow Distribution between Sources and Capital and Operating Costs

“…Unlike the traditional version of the genetic algorithm, the PGA is based on the integer coding of its solutions. The software was implemented in the Java language following the specification of [22]. This system can conduct massive simulations and is integrated with the hydraulic network solver EPANET using the programmer's toolkit [23].…”

“…The main parameters of the proposed PGA are the population size (P), crossover frequency (Pc), and mutation frequency (Pm). We have adopted the optimal parameter calibration range obtained in previous works for the PGA [22,[24][25][26]. To ensure a minimum level of statistical confidence of the results,…”

“…In this regard, evolutionary algorithms have proved to be efficient in handling optimization problems with respect to WDNs, especially when the size of the feasible solution space is extremely large [1,21]. The evaluation of the hydraulic behavior of the resulting network is analyzed using EPANET according to the specifications proposed in [22].…”

Energy-saving Optimal Scheduling of Laminar Cooling Water Supply Pumping Stations in Hot Rolling Mills Considering Complex Intermittent Working Conditions

“…Plants generally ensure the cooling quality of their products at the expense of lower resource utilization. Therefore, we proposed the pump station energy-saving optimal scheduling method [8][9][10].…”

“…Fig 10. Optimization effect of pump station scheduling instructions Fig.10clearly shows that when the water supply from the pump station is equal to the optimized scheduling instruction, water levels of the high cistern during the cooling cycle of this batch strips meet the process requirements, and cooling water overflows have disappeared.…”