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Article(id=1223210586645844939, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223210584024400210, articleNumber=null, orderNo=null, doi=10.20040/j.cnki.1000-7709.2023.20221558, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1659024000000, receivedDateStr=2022-07-29, revisedDate=1661184000000, revisedDateStr=2022-08-23, acceptedDate=null, acceptedDateStr=null, onlineDate=1769565730009, onlineDateStr=2026-01-28, pubDate=1687622400000, pubDateStr=2023-06-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1769565730009, onlineIssueDateStr=2026-01-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1769565730009, creator=13701087609, updateTime=1769565730009, updator=13701087609, issue=Issue{id=1223210584024400210, tenantId=1146029695717560320, journalId=1205116964453384197, year='2023', volume='41', issue='6', 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=1769565729385, creator=13701087609, updateTime=1769593153259, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1223325608164348105, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223210584024400210, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1223325608164348106, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223210584024400210, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=54, endPage=58, ext={EN=ArticleExt(id=1223210587983827954, articleId=1223210586645844939, tenantId=1146029695717560320, journalId=1205116964453384197, language=EN, title=Optimal Operation of Flood Control in Wan’an Reservoir Based on Improved Differential Evolution Algorithm, columnId=1222925283431272938, journalTitle=Water Resources and Power, columnName=HYDROLOGICAL FORECAST AND OPTIMAL SCHEDULING, runingTitle=null, highlight=null, articleAbstract=

The reservoir flood control operation problem has the characteristics of multi-stage, nonlinear and multiple complex constraints. The research of solution method will help to improve the effect of flood control optimal operation. Differential evolution algorithm (DE) has a good effect in the application of reservoir optimal operation, but it is prone to premature convergence and fall into local optimum due to the random search strategy. In order to balance the global and local search ability of DE, this paper proposed an improved differential evolution algorithm SDE, which is used to improve the search ability by considering the differential mutation strategy of fitness ranking and to improve the development ability by random selection mechanism. The related numerical experiments verified that the SDE convergence is significantly better than that of the DE, GA, PSO, and ACOR. The results of case study show that the SDE is better than POA and DE in terms of the feasible solution, calculation time, accuracy, convergence stability and speed. The average flood-peak rate of the SDE is 0.17%-4.26% higher than the POA, and is 0.13%-1.26% higher than the DE.

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水库防洪调度问题具有多阶段、非线性和多重复杂约束特性，研究其高效稳定求解方法有助于提升其防洪优化调度效果。差分进化算法（DE）作为一种启发式智能优化方法在水库优化调度应用中效果较好，但受其随机搜索策略影响搜索易早熟收敛陷入局部最优。为平衡DE的全局和局部搜索能力，提出了一种改进差分进化算法（SDE），通过考虑适应度排序的差分变异策略用于提升搜索能力，并通过随机选择机制用于提升开发能力。相关数值试验验证了SDE收敛能力显著优于粒子群算法（PSO）、遗传算法（GA）、差分进化（DE）、连续域蚁群优化算法（ACOR）。实例分析结果表明，SDE在可行解、计算时间、精度、稳定性和速度指标上均优于逐步优化算法（POA）和DE，平均削峰率较POA提升0.17%~4.26%，较DE提升0.13%~1.26%。

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何中政（1992-），男，博士、讲师，研究方向为复杂水资源系统建模及优化调控，E-mail：
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刘孟桦（1969-），男，副教授，研究方向为水利工程管理，E-mail：

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刘孟桦（1969-），男，副教授，研究方向为水利工程管理，E-mail：

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刘孟桦（1969-），男，副教授，研究方向为水利工程管理，E-mail：

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基于改进差分进化算法的万安水库防洪优化调度
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刘孟桦 1 , 何中政 2a, 2b , 盛卫荣 3 , 熊芳金 1, 2a , 付吉斯 2a, 2b , 尹恒 2a
水电能源科学 | 水情测报与优化调度 2023,41(6): 54-58
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水电能源科学 | 水情测报与优化调度 2023, 41(6): 54-58
基于改进差分进化算法的万安水库防洪优化调度
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刘孟桦1 , 何中政2a, 2b , 盛卫荣3, 熊芳金1, 2a, 付吉斯2a, 2b, 尹恒2a
作者信息
  • 1.江西水利职业学院，江西 南昌 330013
  • 2.a.南昌大学工程建设学院，江西 南昌 330031
  • 2.b.南昌大学鄱阳湖环境与资源利用教育部重点实验室，江西 南昌 330031
  • 3.江西省水文监测中心，江西 南昌 330002

    • 刘孟桦（1969-），男，副教授，研究方向为水利工程管理，E-mail：

通讯作者:

何中政（1992-），男，博士、讲师，研究方向为复杂水资源系统建模及优化调控，E-mail：
Optimal Operation of Flood Control in Wan’an Reservoir Based on Improved Differential Evolution Algorithm
Meng-hua LIU1 , Zhong-zheng HE2a, 2b , Wei-rong SHENG3, Fang-jin XIONG1, 2a, Ji-si FU2a, 2b, Heng YIN2a
Affiliations
  • 1.Jiangxi Water Resources Institute, Nanchang 330013, China
  • 2a.School of Infrastructure Engineering, Nanchang University, Nanchang 330031, China
  • 2b.Key Laboratory of Poyang Lake Environment and Resources Utilization, Ministry of Education, Nanchang University, Nanchang 330031, China
  • 3.Hydrological Monitoring Center of Jiangxi Province, Nanchang 330002, China
出版时间: 2023-06-25 doi: 10.20040/j.cnki.1000-7709.2023.20221558
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水库防洪调度问题具有多阶段、非线性和多重复杂约束特性，研究其高效稳定求解方法有助于提升其防洪优化调度效果。差分进化算法（DE）作为一种启发式智能优化方法在水库优化调度应用中效果较好，但受其随机搜索策略影响搜索易早熟收敛陷入局部最优。为平衡DE的全局和局部搜索能力，提出了一种改进差分进化算法（SDE），通过考虑适应度排序的差分变异策略用于提升搜索能力，并通过随机选择机制用于提升开发能力。相关数值试验验证了SDE收敛能力显著优于粒子群算法（PSO）、遗传算法（GA）、差分进化（DE）、连续域蚁群优化算法（ACOR）。实例分析结果表明，SDE在可行解、计算时间、精度、稳定性和速度指标上均优于逐步优化算法（POA）和DE，平均削峰率较POA提升0.17%~4.26%，较DE提升0.13%~1.26%。

万安水库  /  防洪优化调度  /  差分进化（DE）  /  最大削峰准则  /  罚函数

The reservoir flood control operation problem has the characteristics of multi-stage, nonlinear and multiple complex constraints. The research of solution method will help to improve the effect of flood control optimal operation. Differential evolution algorithm (DE) has a good effect in the application of reservoir optimal operation, but it is prone to premature convergence and fall into local optimum due to the random search strategy. In order to balance the global and local search ability of DE, this paper proposed an improved differential evolution algorithm SDE, which is used to improve the search ability by considering the differential mutation strategy of fitness ranking and to improve the development ability by random selection mechanism. The related numerical experiments verified that the SDE convergence is significantly better than that of the DE, GA, PSO, and ACOR. The results of case study show that the SDE is better than POA and DE in terms of the feasible solution, calculation time, accuracy, convergence stability and speed. The average flood-peak rate of the SDE is 0.17%-4.26% higher than the POA, and is 0.13%-1.26% higher than the DE.

Wan’an reservoir  /  optimal operation of flood control  /  differential evolution (DE)  /  maximum flood-peak rate criterion  /  penalty function
刘孟桦, 何中政, 盛卫荣, 熊芳金, 付吉斯, 尹恒. 基于改进差分进化算法的万安水库防洪优化调度. 水电能源科学, 2023 , 41 (6) : 54 -58 . DOI: 10.20040/j.cnki.1000-7709.2023.20221558
Meng-hua LIU, Zhong-zheng HE, Wei-rong SHENG, Fang-jin XIONG, Ji-si FU, Heng YIN. Optimal Operation of Flood Control in Wan’an Reservoir Based on Improved Differential Evolution Algorithm[J]. Water Resources and Power, 2023 , 41 (6) : 54 -58 . DOI: 10.20040/j.cnki.1000-7709.2023.20221558
基金
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  • 江西省水利厅科技项目(202223YBKT43)
  • 江西省自然科学基金(20212BAB214065; 20224BAB204075)
  • 国家自然科学基金(52209024)
2023年第41卷第6期
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doi: 10.20040/j.cnki.1000-7709.2023.20221558
  • 接收时间：2022-07-29
  • 首发时间：2026-01-28
  • 出版时间：2023-06-25
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  • 收稿日期：2022-07-29
  • 修回日期：2022-08-23
基金
江西省水利厅科技项目(202223YBKT43)
江西省自然科学基金(20212BAB214065; 20224BAB204075)
国家自然科学基金(52209024)
作者信息
    1.江西水利职业学院，江西 南昌 330013
    2.a.南昌大学工程建设学院，江西 南昌 330031
    2.b.南昌大学鄱阳湖环境与资源利用教育部重点实验室，江西 南昌 330031
    3.江西省水文监测中心，江西 南昌 330002

通讯作者:

何中政（1992-），男，博士、讲师，研究方向为复杂水资源系统建模及优化调控，E-mail：
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https://castjournals.cast.org.cn/joweb/sdnykx/CN/10.20040/j.cnki.1000-7709.2023.20221558
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