Feasibility-guided evolutionary optimization of pump station design and operation in water networks
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| Publicado no: | Scientific Reports (Nature Publisher Group) vol. 15, no. 1 (2025), p. 34455-34470 |
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| 100 | 1 | |a Faúndez-Lizama, Thalía |u Master’s Program in Operations Management, Faculty of Engineering, Universidad de Talca, 3340000, Curicó, Chile (ROR: https://ror.org/01s4gpq44) (GRID: grid.10999.38) (ISNI: 0000 0001 0036 2536) | |
| 245 | 1 | |a Feasibility-guided evolutionary optimization of pump station design and operation in water networks | |
| 260 | |b Nature Publishing Group |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Pumping stations are critical elements of water distribution networks (WDNs), as they ensure the required pressure for supply but represent the highest energy consumption within these systems. In response to increasing water scarcity and the demand for more efficient operations, this study proposes a novel methodology to optimize both the design and operation of pumping stations. The approach combines Feasibility-Guided Evolutionary Algorithms (FGEAs) with a Feasibility Predictor Model (FPM), a machine learning-based classifier designed to identify feasible solutions and filter out infeasible ones before performing hydraulic simulations. This significantly reduces the computational burden. The methodology is validated through a real-scale case study using four FGEAs, each incorporating a different classification algorithm: Extreme Gradient Boosting, Random Forest, K-Nearest Neighbors, and Decision Tree. Results show that the number of objective function evaluations was reduced from 50,000 to fewer than 25,000. Additionally, The FGEAs based on Extreme Gradient Boosting and Random Forest outperformed the original algorithm in terms of objective value. These results confirm the effectiveness of integrating machine learning into evolutionary optimization for solving complex engineering problems and highlight the potential of this methodology to reduce operational costs while improving computational efficiency in WDNs. | |
| 653 | |a Mathematical models | ||
| 653 | |a Algorithms | ||
| 653 | |a Optimization techniques | ||
| 653 | |a Drainage | ||
| 653 | |a Civil engineering | ||
| 653 | |a Feasibility | ||
| 653 | |a Machine learning | ||
| 653 | |a Computer applications | ||
| 653 | |a Energy consumption | ||
| 653 | |a Water distribution | ||
| 653 | |a Learning algorithms | ||
| 653 | |a Case studies | ||
| 653 | |a Water quality | ||
| 653 | |a Scheduling | ||
| 653 | |a Simulation | ||
| 653 | |a Infrastructure | ||
| 653 | |a Costs | ||
| 653 | |a Genetic algorithms | ||
| 653 | |a Objective function | ||
| 653 | |a Water scarcity | ||
| 653 | |a Methods | ||
| 653 | |a Pumping stations | ||
| 653 | |a Hydraulics | ||
| 653 | |a Economic | ||
| 700 | 1 | |a Gutiérrez-Bahamondes, Jimmy H. |u Department of Computer Science, Universidad de Talca, 3340000, Curicó, Chile (ROR: https://ror.org/01s4gpq44) (GRID: grid.10999.38) (ISNI: 0000 0001 0036 2536) | |
| 700 | 1 | |a Gajardo-Sepúlveda, Nicolás |u Master’s Program in Operations Management, Faculty of Engineering, Universidad de Talca, 3340000, Curicó, Chile (ROR: https://ror.org/01s4gpq44) (GRID: grid.10999.38) (ISNI: 0000 0001 0036 2536) | |
| 700 | 1 | |a Mora-Meliá, Daniel |u Department of Hydraulic Engineering and Environment, Universitat Politècnica de València, 46022, Valencia, Spain (ROR: https://ror.org/01460j859) (GRID: grid.157927.f) (ISNI: 0000 0004 1770 5832) | |
| 773 | 0 | |t Scientific Reports (Nature Publisher Group) |g vol. 15, no. 1 (2025), p. 34455-34470 | |
| 786 | 0 | |d ProQuest |t Science Database | |
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