{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T18:51:35Z","timestamp":1760986295496,"version":"build-2065373602"},"reference-count":23,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2024,12,6]],"date-time":"2024-12-06T00:00:00Z","timestamp":1733443200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62476006","2023AFA049","2024JBZD0007"],"award-info":[{"award-number":["62476006","2023AFA049","2024JBZD0007"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Hubei Provincial Natural Science Foundation of China","award":["62476006","2023AFA049","2024JBZD0007"],"award-info":[{"award-number":["62476006","2023AFA049","2024JBZD0007"]}]},{"name":"Fundamental Research Funds of the AUST","award":["62476006","2023AFA049","2024JBZD0007"],"award-info":[{"award-number":["62476006","2023AFA049","2024JBZD0007"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Data"],"abstract":"<jats:p>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\u2019s 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\u2019s 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\u2019 behaviors.<\/jats:p>","DOI":"10.3390\/data9120142","type":"journal-article","created":{"date-parts":[[2024,12,6]],"date-time":"2024-12-06T09:55:20Z","timestamp":1733478920000},"page":"142","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Nearest-Better Network-Assisted Fitness Landscape Analysis of Contaminant Source Identification in Water Distribution Network"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1685-7469","authenticated-orcid":false,"given":"Yiya","family":"Diao","sequence":"first","affiliation":[{"name":"School of Automation, China University of Geosciences, Wuhan 430074, China"},{"name":"Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan 430074, China"},{"name":"Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9222-0702","authenticated-orcid":false,"given":"Changhe","family":"Li","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Anhui University of Science & Technology, Hefei 232001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sanyou","family":"Zeng","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7222-4917","authenticated-orcid":false,"given":"Shengxiang","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Informatics, De Montfort University, Leicester LE1 9BH, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4623","DOI":"10.1007\/s11269-013-0431-z","article-title":"Localization of Contamination Sources in Drinking Water Distribution Systems: A Method Based on Successive Positive Readings of Sensors","volume":"27","author":"Costa","year":"2013","journal-title":"Water Resour. 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