Optimal Design of Water Supply Network Based on Adaptive Penalty Function and Improved Genetic Algorithm

Ding, Kun; Ni, Yong; Fan, Lingfeng; Sun, Tian-Le

doi:10.1155/2022/8252086

Cited by 8 publications

(3 citation statements)

References 34 publications

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“…Compared with traditional optimization algorithms, GA usually performs better when solving complex combinatorial optimization problems. In recent decades, GA has been widely applied to various optimization problems [42][43][44]. However, the traditional GA requires many iterations for the best solution, which results in a long optimization time.…”

Section: Optimization Algorithm 221 Related Workmentioning

confidence: 99%

Optimization of Fracturing Parameters by Modified Genetic Algorithm in Shale Gas Reservoir

Zhou

Ran

2023

Energies

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Shale gas reservoirs have extremely low porosity and permeability, making them challenging to exploit. The best method for increasing recovery in shale gas reservoirs is horizontal well fracturing technology. Hence, fracturing parameter optimization is necessary to enhance shale gas horizontal fracturing well production. Traditional optimization methods, however, cannot meet the requirements for overall optimization of fracturing parameters. As for intelligent optimization algorithms, most have excellent global search capability but incur high computation costs, which limits their usefulness in real-world engineering applications. Thus, a modified genetic algorithm combined based on the Spearman correlation coefficient (SGA) is proposed to achieve the rapid optimization of fracturing parameters. SGA determines the crossover and mutation rates by calculating the Spearman correlation coefficient instead of randomly determining the rates like GA does, so that it could quickly converge to the optimal solution. Within a particular optimization time, SGA could perform better than GA. In this study, a production prediction model is established by the XGBoost algorithm based on the dataset obtained by simulating the shale gas multistage fracturing horizontal well development. The result shows that the XGBoost model performs well in predicting shale gas fracturing horizontal well production. Based on the trained XGBoost model, GA, SGA, and SGD were used to optimize the fracturing parameters with the 30-day cumulative production as the optimization objective. This process has conducted nine fracturing parameter optimization tests under different porosity and permeability conditions. The results show that, compared with GA and SGD, SGA has faster speed and higher accuracy. This study’s findings can help optimize the fracturing parameters faster, resulting in improving the production of shale gas fracturing horizontal wells.

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Section: Optimization Algorithm 221 Related Workmentioning

confidence: 99%

Optimization of Fracturing Parameters by Modified Genetic Algorithm in Shale Gas Reservoir

Zhou

Ran

2023

Energies

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“…GAs are biologically motivated adaptive computer techniques based on natural selection and genetic operators as Wang (1991), Naghibi et al (2017), Ding et al (2022), Shirajuddin et al (2023), and Guan et al (2023). These algorithms are often suggested to solve complex optimization problems by Zanfei et al (2020), Meirelles et al (2017), Di Nardo et al (2014), Do et al (2016), and Mambretti and Orsi (2016).…”

Section: Overall Conceptmentioning

confidence: 99%

Enhancing the EPANET Hydraulic Model through Genetic Algorithm Optimization of Pipe Roughness Coefficients

Shiu,

Chung,

Chiang

2023

Water Resour Manage

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Calibrating hydraulic models for water distribution systems (WDS) is crucial during model-building, particularly in determining the roughness coefficients of pipes. However, using a single roughness coefficient based solely on pipe material can lead to significant variations in frictional head losses. To address this issue and enhance computational efficiency, this study utilized genetic algorithm (GA) 1 for optimizing roughness coefficients with the Environmental Protection Agency Network Evaluation Tool (EPANET) hydraulic model. EPANET-GA further considers the spatial characteristics of pipes. The study incorporated an automated calibration process and a user graphic interface to analyze the water head pressures of WDS nodes for the Zhonghe and Yonghe districts. The model was optimized with 34,783 node items, 30,940 pipes, and 140 field measurements. Results reveal that the optimized roughness coefficient produces a high correlation coefficient (0.90) with the measured data in a time slot. Besides, a low standard error (8.93%) was achieved for 24-hour predictions. Furthermore, in the Shelin-Beitou district, the consideration of spatial characteristics was incorporated as constraints during the calibration process. The improved outcomes indicate that the EPANET-GA is a reliable reference for WDS design and routing scenarios in practice.

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“…Previous studies have used genetic algorithms to optimize duct diameter combinations [19][20][21]. There are various improved genetic algorithms such as using adaptive penalty functions [22], improved crossover operator [23] and mutation operators [24,25], as well as in combination with hydraulic simulation software such as EPANET 2.2 [26]. However, the optimization objects of previous studies are typically urban water supply networks, while this study focuses on DOV networks.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Dual-Design Operation Ventilation System Network Based on Improved Genetic Algorithm

Feng,

Zhu,

Feng

et al. 2023

Energies

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The COVID-19 pandemic has emphasized the crucial role of ventilation systems in mitigating cross-infections, especially in infectious-disease hospitals. This study introduces a dual-design operation ventilation system that can operate under two sets of ventilation conditions for normal and epidemic times. A challenge is optimizing duct diameters for required airflow while maintaining hydraulic balance. We designed an improved genetic algorithm with an adaptive penalty factor and velocity constraint, as well as the improved crossover probability and mutation probability. The improved genetic algorithm is suitable for ventilation system networks, which can find a better combination of air duct diameters to improve the hydraulic balance rate and reduce the usage of air valves, resulting in efficient hydraulic balancing commissioning. A supply air ventilation system of an actual hospital in China was selected as a case study, and the number of imbalanced air ducts was reduced from 14 to 4. Compared with the traditional genetic algorithm, it has a faster search speed and a better global search ability, which is effective for the optimal design of ventilation system networks.

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Optimal Design of Water Supply Network Based on Adaptive Penalty Function and Improved Genetic Algorithm

Cited by 8 publications

References 34 publications

Optimization of Fracturing Parameters by Modified Genetic Algorithm in Shale Gas Reservoir

Optimization of Fracturing Parameters by Modified Genetic Algorithm in Shale Gas Reservoir

Enhancing the EPANET Hydraulic Model through Genetic Algorithm Optimization of Pipe Roughness Coefficients

Optimization of Dual-Design Operation Ventilation System Network Based on Improved Genetic Algorithm

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