Article(id=1254362828962464585, tenantId=1146029695717560320, journalId=1254119036117037056, issueId=1254362823425974931, articleNumber=null, orderNo=null, doi=10.13788/j.cnki.cbgc.2026.03.19, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1746806400000, receivedDateStr=2025-05-10, revisedDate=1758470400000, revisedDateStr=2025-09-22, acceptedDate=null, acceptedDateStr=null, onlineDate=1776993003358, onlineDateStr=2026-04-24, pubDate=1774368000000, pubDateStr=2026-03-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1776993003358, onlineIssueDateStr=2026-04-24, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1776993003358, creator=13701087609, updateTime=1776993003358, updator=13701087609, issue=Issue{id=1254362823425974931, tenantId=1146029695717560320, journalId=1254119036117037056, year='2026', volume='48', issue='3', pageStart='1', pageEnd='190', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1776993002036, creator=13701087609, updateTime=1776993258606, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1254363899764077151, tenantId=1146029695717560320, journalId=1254119036117037056, issueId=1254362823425974931, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1254363899764077152, tenantId=1146029695717560320, journalId=1254119036117037056, issueId=1254362823425974931, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=170, endPage=180, ext={EN=ArticleExt(id=1254362829587415890, articleId=1254362828962464585, tenantId=1146029695717560320, journalId=1254119036117037056, language=EN, title=Improved Hybrid PSO-Based Dynamic Cable Optimization Design, columnId=1254362828626915663, journalTitle=Ship Engineering, columnName=Ocean Engineering, runingTitle=null, highlight=null, articleAbstract=
[Purpose] To effectively reduce fatigue damage, a reasonable dynamic cable design is required.
[Method] An optimization model based on an improved hybrid particle swarm optimization algorithm is established. It employ MATLAB to develop a genetic-chaotic particle swarm dynamic factor optimization algorithm and utilize the Orcaflex software for the overall design and optimization of platform dynamic cables. The optimization problem of deepwater dynamic cables is treated as the objective function, with parameters such as cable length, buoyancy block and counterweight block positions, and spacing as optimization variables. Building upon the foundation of the standard particle swarm algorithm and integrating genetic algorithms, it effectively prevent dynamic cable optimization parameters from falling into local optima. Chaotic initialization of initial particles is applied to ensure a uniform distribution in high-dimensional solution spaces. Dynamic inertia weight factors and learning factors are introduced to balance global and local search capabilities during optimization. Adhering to the Pareto principle, It formulate an objective function to facilitate multi-objective constrained optimization. The improved optimization algorithm shows better performance in terms of convergence, accuracy, and convergence speed.
[Result] It quickly and effectively balances the relationship between the maximum axial tension and the minimum bending radius of the cable and pipe, achieves the optimal design.
[Conclusion] It provides strong support and guidance for practical engineering applications.
, correspAuthors=Qingfeng DUAN, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, authorCompany=null, fund=null, authors=null, authorsList=Le SUN, Qingfeng DUAN, Chen AN, Menglan DUAN), CN=ArticleExt(id=1254362832049472357, articleId=1254362828962464585, tenantId=1146029695717560320, journalId=1254119036117037056, language=CN, title=基于改进混合PSO动态管缆优化设计, columnId=1254362829197341017, journalTitle=船舶工程, columnName=海洋工程, runingTitle=null, highlight=null, articleAbstract=
[目的] 为有效减少疲劳损失,须采用合理的动态管缆设计。
[方法] 基于改进的混合粒子群优化算法构建优化模型,该算法利用MATLAB进行遗传-混沌粒子群动态因子优化,并通过调用Orcaflex软件,对平台动态管缆进行总体设计与优化。以深水动态管缆的优化为核心构建目标函数,系链、浮力块和配重块的位置、间距为优化参数,标准粒子群算法为基础,结合遗传算法,有效避免动态管缆优化参数陷入局部最优;对初始粒子进行混沌初始化处理,确保初始粒子可在高维解空间中分布均匀;引入动态惯性权重因子和学习因子,实现优化过程中全局搜索与局部搜索能力的动态平衡;根据帕累托最优原则,构建适应度函数,进行多目标约束优化。
[结果] 改进的优化算法在收敛性、准确性、收敛速度方面都表现出更优的性能,能快速、有效地平衡管缆最大轴向张力与最小弯曲半径之间的关系,实现最优设计的目标。
[结论] 为实际工程应用提供有力的技术支持和理论指导。
, correspAuthors=段青峰, authorNote=null, correspAuthorsNote=
段青峰(1988—),男,博士、研究员。研究方向:油气管道。
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本刊已许可中国学术期刊(光盘版)电子杂志社在中国知网及其系列数据库产品中以数字化方式复制、汇编、发行、信息网络传播本刊全文,本刊著作权使用费与本刊稿酬一并支付;本刊所刊登文章均无知识产权争议,不涉及泄露国家秘密、技术秘密或商业秘密。作者向本刊提交文章发表的行为即视为同意我编辑部上述声明,文责自负。, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=6hm7xngeRO/7VgY9+82Ojw==, magXml=a7wx3Ah5y0/mw4wPn2g/DA==, pdfUrl=null, pdf=OzhSE8AwA3TUwgui3Q0IiA==, pdfFileSize=3212508, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=hPjXtP3R4ntv4QAYmYSoKA==, mapNumber=null, authorCompany=null, fund=null, authors=
孙乐(1996—),男,硕士、三级工程师。研究方向:油气管道。
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孙乐(1996—),男,硕士、三级工程师。研究方向:油气管道。
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