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Nearest-Better Network-Assisted Fitness Landscape Analysis of Contaminant Source Identification in Water Distribution Network

Author

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  • Yiya Diao

    (School of Automation, China University of Geosciences, Wuhan 430074, China
    Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan 430074, China
    Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education, Wuhan 430074, China)

  • Changhe Li

    (School of Artificial Intelligence, Anhui University of Science & Technology, Hefei 232001, China)

  • Sanyou Zeng

    (School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, China)

  • Shengxiang Yang

    (School of Computer Science and Informatics, De Montfort University, Leicester LE1 9BH, UK)

Abstract

Contaminant Source Identification in Water Distribution Network (CSWIDN) is critical for ensuring public health, and optimization algorithms are commonly used to solve this complex problem. However, these algorithms are highly sensitive to the problem’s landscape features, which has limited their effectiveness in practice. Despite this, there has been little experimental analysis of the fitness landscape for CSWIDN, particularly given its mixed-encoding nature. This study addresses this gap by conducting a comprehensive fitness landscape analysis of CSWIDN using the Nearest-Better Network (NBN), the only applicable method for mixed-encoding problems. Our analysis reveals for the first time that CSWIDN exhibits the landscape features, including neutrality, ruggedness, modality, dynamic change, and separability. These findings not only deepen our understanding of the problem’s inherent landscape features but also provide quantitative insights into how these features influence algorithm performance. Additionally, based on these insights, we propose specific algorithm design recommendations that are better suited to the unique challenges of the CSWIDN problem. This work advances the knowledge of CSWIDN optimization by both qualitatively characterizing its landscape and quantitatively linking these features to algorithms’ behaviors.

Suggested Citation

  • Yiya Diao & Changhe Li & Sanyou Zeng & Shengxiang Yang, 2024. "Nearest-Better Network-Assisted Fitness Landscape Analysis of Contaminant Source Identification in Water Distribution Network," Data, MDPI, vol. 9(12), pages 1-20, December.
    • Handle: RePEc:gam:jdataj:v:9:y:2024:i:12:p:142-:d:1538269
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    References listed on IDEAS

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    1. D. Costa & L. Melo & F. Martins, 2013. "Localization of Contamination Sources in Drinking Water Distribution Systems: A Method Based on Successive Positive Readings of Sensors," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(13), pages 4623-4635, October.
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