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

Author

Listed:
  • Kun Ding
  • Yong Ni
  • Lingfeng Fan
  • Tian-Le Sun
  • Tabasam Rashid

Abstract

In view of the shortcomings of water supply network optimization design based on the traditional genetic algorithm in water supply safety and economy, an improved crossover operator adaptive algorithm and penalty function are proposed to improve the traditional genetic algorithm, which can effectively solve the problem of local optimal solution caused by too early convergence of the traditional genetic algorithm in pipe network optimization design. Taking a typical annular water supply network as an example, the calculation results show that the economy of the design scheme of the improved genetic algorithm is better than the traditional genetic algorithm, which fully shows that the improved genetic algorithm is practical and effective for the optimal design of water supply network.

Suggested Citation

  • Kun Ding & Yong Ni & Lingfeng Fan & Tian-Le Sun & Tabasam Rashid, 2022. "Optimal Design of Water Supply Network Based on Adaptive Penalty Function and Improved Genetic Algorithm," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-8, March.
    • Handle: RePEc:hin:jnlmpe:8252086
    DOI: 10.1155/2022/8252086
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    Cited by:

    1. Yanbo Feng & Han Zhu & Xiwen Feng & Qianru Chen & Xiangyu Sun & Zhengrong Li, 2023. "Optimization of Dual-Design Operation Ventilation System Network Based on Improved Genetic Algorithm," Energies, MDPI, vol. 16(24), pages 1-15, December.
    2. Chia-Cheng Shiu & Chih-Chung Chung & Tzuping Chiang, 2024. "Enhancing the EPANET Hydraulic Model through Genetic Algorithm Optimization of Pipe Roughness Coefficients," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(1), pages 323-341, January.
    3. Xin Zhou & Qiquan Ran, 2023. "Optimization of Fracturing Parameters by Modified Genetic Algorithm in Shale Gas Reservoir," Energies, MDPI, vol. 16(6), pages 1-13, March.

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