Article(id=1223201255699370463, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223201250133524577, articleNumber=null, orderNo=null, doi=10.20040/j.cnki.1000-7709.2023.20221893, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1662912000000, receivedDateStr=2022-09-12, revisedDate=1665763200000, revisedDateStr=2022-10-15, acceptedDate=null, acceptedDateStr=null, onlineDate=1769563505339, onlineDateStr=2026-01-28, pubDate=1690214400000, pubDateStr=2023-07-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1769563505339, onlineIssueDateStr=2026-01-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1769563505339, creator=13701087609, updateTime=1769563505339, updator=13701087609, issue=Issue{id=1223201250133524577, tenantId=1146029695717560320, journalId=1205116964453384197, year='2023', volume='41', issue='7', pageStart='1', pageEnd='220', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1769563504012, creator=13701087609, updateTime=1769563583713, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1223201584469885927, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223201250133524577, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1223201584469885928, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223201250133524577, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=132, endPage=136, ext={EN=ArticleExt(id=1223201256039109107, articleId=1223201255699370463, tenantId=1146029695717560320, journalId=1205116964453384197, language=EN, title=Multi-objective Optimization Model for Leakage Location of Water Supply Network Based on NSGA-Ⅱ, columnId=1222925283779400191, journalTitle=Water Resources and Power, columnName=WATER CONSERVANCY AND HYDROPOWER ENGINEERING, runingTitle=null, highlight=null, articleAbstract=

Aiming at the leakage problem of urban water supply network, the traditional leakage location model is solved by single objective optimization method. Its objective function is composed of the weighted sum of optimization objectives. Improper value of weight factor will degrade the model positioning result. This paper proposed a multi-objective optimization model for leakage location of water supply network based on NSGA-Ⅱ. In this model, the number of leakage point and diffuser coefficient were taken as decision variables, and the minimum error between the actual monitoring value and the simulation value of pressure and flow monitoring point was taken as the optimization objective, and NSGA-Ⅱ algorithm was used to find the optimal solution. In order to verify the effectiveness of the model, it was applied to the actual water supply network in a residential area. Considering the common fault conditions in the actual operation of the pipe network, the leakage accidents of single node and two nodes were respectively simulated and located. Compared with the two widely used leak location models, the results show that the multi-objective optimization model based on NSGA-Ⅱ can accurately and stably locate the nearby area where the actual leak occurs.

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针对城市供水管网漏失问题，传统漏损定位模型采用单目标优化方法求解，其目标函数由各优化目标加权求和构成，权重因子取值不当会劣化模型定位结果。为此，提出一种基于NSGA-Ⅱ的供水管网漏损定位多目标优化模型。该模型以泄漏点编号和扩散器系数作为决策变量，分别以压力和流量监测点的实际监测值与仿真值之间的误差最小作为优化目标，采用NSGA-Ⅱ算法进行寻优求解。为验证该模型的有效性，将其应用于某居民区实际供水管网，考虑管网实际运行中常见的故障工况，分别对单节点和两节点漏损事故进行模拟定位。与目前应用较广泛的两种漏损定位模型进行对比，结果显示，基于NSGA-Ⅱ的漏损定位多目标优化模型可以准确且稳定地定位到实际发生漏损的附近区域。

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苏彦博（1998-），男，硕士研究生，研究方向为供排水系统分析与优化，E-mail：suyanbo98@163.com

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苏彦博（1998-），男，硕士研究生，研究方向为供排水系统分析与优化，E-mail：suyanbo98@163.com

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