Multi-objective optimization of water distribution networks using particle swarm optimization

Surco, Douglas F.; Macowski, Diogo H.; Cardoso, Flávia A.R.; Vecchi, Thelma Pretel Brandão; Ravagnani, Mauro A.S.S.

doi:10.5004/dwt.2021.26944

Cited by 6 publications

(9 citation statements)

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“…Surco et al constructed a multi-objective optimization model by minimizing the installation cost of WDN and the energy cost of water supply system as the objective function, the improved PSO algorithm was used to solve the problem. The results show that the results obtained by this algorithm are the same or better than those obtained in previous literature [ 15 ]. Zhang et al constructed an optimization model of water distribution network by considering multiple factors such as hydraulic power, water quality and economy, and NSGA-II was used to solve the problem.…”

Section: Introductionsupporting

confidence: 75%

Multi-objective optimization of water distribution networks based on non-dominated sequencing genetic algorithm

Yan

Yang

et al. 2022

PLoS ONE

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Due to the conflict between reducing cost and improving water supply performance, how to select the appropriate pipe diameter is a current challenge. In this paper, the problem is transformed into a multi-objective optimization problem, and the evolutionary genetic optimization algorithm is used to solve the problem to determine the optimal selection of pipe diameter in the pipe network. To solve this problem, the evolutionary genetic algorithm was coupled with EPANET hydraulic simulation software in Python environment. The results show that NSGA-II and NSGA-III perform better in two typical case tests. Moreover, the increase of the objective functions will lead to an increase in the amount of data in the optimal solution set, and will affect the optimal value of each objective function. That shows that the balance between the economy and reliability of water supply can be successfully found by coupling the hydraulic model and the multi-objective optimization algorithm, which can provide an auxiliary decision for enterprises.

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Section: Introductionsupporting

confidence: 75%

Multi-objective optimization of water distribution networks based on non-dominated sequencing genetic algorithm

Yan

Yang

et al. 2022

PLoS ONE

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“…Considering the topological diversity that can exist in WDN, some demand nodes, sometimes, do not have adequate pressure and it is necessary to use pumping stations to solve the problem. Considering n a years, a set of annual costs can be actualized, by using an interest rate and a unitary rate of increase in the energy costs, given by (Surco et al 2021):…”

Section: Methodsmentioning

confidence: 99%

“…Table 4 presents a comparison with the literature results. Different approaches were used in the works of Gomes et al (2009), who used an iterative approach and a ¼ 10:67, b ¼ 1:852 , and g ¼ 4:871, and Surco et al (2021), who used PSO to solve the problem with the aid of hydraulic simulators and a ¼ 10:667, and the same values for b and g.…”

Section: Case Studymentioning

confidence: 99%

Optimization of installation and energy costs in water distribution systems with unknown flow directions

2022

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Water distribution networks (WDNs) are an important part of water distribution systems and are responsible for water transportation from the reservoirs to the demand nodes at adequate pressure and velocity. In the present paper, the synthesis of WDN is treated as an optimization problem with a mixed integer nonlinear programming formulation. The objective function to be minimized is the total network cost, considering installation and energy costs, with unknown flow directions, which is the novelty in the paper. Disjunctive programming and linearization techniques are used in the model formulation to avoid nonlinear and nonconvex problems. Two case studies are used to test the model's applicability. Results show that operational costs can represent a significant part of the total cost in sustainable networks. In the first case study, the total cost was better than the literature results (US$ 2,272,538.85 vs. US$ 2,272,387.49) and the operational costs represent ¼ of the total WDN costs. In the second case study, the operation cost corresponds to almost 2/3 of the total WDN cost. These results show the importance of considering operational costs in the WDN design. Also, the consideration of unknown flow directions can lead to better results for the network topology.

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“…Stochastic approaches are used in largescale problems, where deterministic approaches normally fail. Some important methods used are Particle Swarm Optimization, in Ezzeldin et al (2014), Surco et al (2017) and Surco et al (2021), Harmony Search (HS) in Geem (2009), Simple Benchmarking Algorithm, in Shende and Chau (2019), Whale Optimization Algorithm, in Ezzeldin and Djebedjian (2020) and Genetic Algorithm, in Reca et al (2017), Sangroula et al (2022), Egito et al (2023), Shekofteh et al (2023) and Parvaze et al (2023).…”

Section: Literature Reviewmentioning

confidence: 99%

Water Distribution Networks Optimization Considering Uncertainties in the Demand Nodes

Cassiolato,

Ruiz-Femenia,

Salcedo-Diaz

et al. 2024

Water Resour Manage

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The fluctuation in the consumption of treated water is a situation that distribution networks gradually face. In times of greater demand, this consumption tends to suffer unnecessary impacts due to the lack of water. The uncertainty that occurs in water consumption can be mathematically modeled by a finite set of scenarios generated by a normal distribution and attributed to the network design. This study presents an optimization model to minimize network installation and operation costs under uncertainties in water demands. A Mixed Integer Nonlinear Programming model is proposed, considering the water flow directions in the pipes as unknown. A deterministic approach is used to solve the problem in three steps: First, the problem is solved with a nominal value for each uncertain parameter. In the second stage, the problem is solved for all scenarios, with the independent variables of the scenario being fixed and obtained from the solution reached in the first stage, known as the deterministic solution. Finally, all scenarios are solved without fixing any variable values, in a stochastic approach. Two case studies were used to test the applicability of the model and global optimization techniques were used to solve the problem. The results show that the stochastic solution can lead to optimal solutions for robust and flexible water distribution networks, capable of working under different conditions, considering the uncertainties of node demand and variable pipe directions.

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Multi-objective optimization of water distribution networks using particle swarm optimization

Cited by 6 publications

References 0 publications

Multi-objective optimization of water distribution networks based on non-dominated sequencing genetic algorithm

Multi-objective optimization of water distribution networks based on non-dominated sequencing genetic algorithm

Optimization of installation and energy costs in water distribution systems with unknown flow directions

Water Distribution Networks Optimization Considering Uncertainties in the Demand Nodes

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