Article(id=1222963544312763150, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1222963536863678929, articleNumber=null, orderNo=null, doi=10.20040/j.cnki.1000-7709.2023.20222126, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1663084800000, receivedDateStr=2022-09-14, revisedDate=1667577600000, revisedDateStr=2022-11-05, acceptedDate=null, acceptedDateStr=null, onlineDate=1769506830527, onlineDateStr=2026-01-27, pubDate=1682352000000, pubDateStr=2023-04-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1769506830527, onlineIssueDateStr=2026-01-27, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1769506830527, creator=13701087609, updateTime=1769506830527, updator=13701087609, issue=Issue{id=1222963536863678929, tenantId=1146029695717560320, journalId=1205116964453384197, year='2023', volume='41', issue='4', 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=1769506828752, creator=13701087609, updateTime=1769508076664, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1222968771057279321, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1222963536863678929, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1222968771057279322, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1222963536863678929, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=97, endPage=101, ext={EN=ArticleExt(id=1222963546741265280, articleId=1222963544312763150, tenantId=1146029695717560320, journalId=1205116964453384197, language=EN, title=Research on Peak Shaving Scheduling of Cascade Reservoir Groups Based on Improved Intelligent Water Drop Algorithm, columnId=1222925283431272938, journalTitle=Water Resources and Power, columnName=HYDROLOGICAL FORECAST AND OPTIMAL SCHEDULING, runingTitle=null, highlight=null, articleAbstract=

Aiming at the shortcomings of insufficient population diversity, easy to fall into local optimum, and slow initial solution speed of intelligent water drops algorithm (IWD), an improved IWD algorithm (IIWD) was proposed, which introduces the Logistic chaotic initialization method and the differential variation operator of the difference evolution algorithm (DE), and improves the neuronal update mode of the self-organizing mapping algorithm (SOM) and introduces it into the sub-update process of the optimal individual of the IWD algorithm. The IIWD was applied to the peak shaving scheduling of cascade reservoir groups. Compared with IWD and DE, the efficiency of the IIWD was verified.

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针对智能水滴算法（IWD算法）存在种群多样性不足、易陷入局部最优、初期求解速度慢等缺陷，提出了改进的智能水滴算法（IIWD算法），该算法引入了Logistic混沌初始化方式及差分进化算法（DE算法）的差分变异算子，同时改进了自组织映射算法（SOM算法）的神经元更新方式并将其引入到IWD算法最优个体的次更新过程中，并将IIWD算法应用于梯级水库群调峰调度中，同时与IWD与DE算法进行对比，验证了该算法的高效性。

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王奇（1998-），男，硕士研究生，研究方向为水库调度，E-mail：wang_qi14@ctg.com.cn

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王奇（1998-），男，硕士研究生，研究方向为水库调度，E-mail：wang_qi14@ctg.com.cn

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