Article(id=1311351242568332133, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1311351152067830064, articleNumber=null, orderNo=null, doi=10.20040/j.cnki.1000-7709.2026.20250286, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1740067200000, receivedDateStr=2025-02-21, revisedDate=1742054400000, revisedDateStr=2025-03-16, acceptedDate=1743782400000, acceptedDateStr=2025-04-05, onlineDate=1790580099955, onlineDateStr=2026-09-28, pubDate=1769270400000, pubDateStr=2026-01-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790580099955, onlineIssueDateStr=2026-09-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790580099955, creator=13701087609, updateTime=1790580099955, updator=13701087609, issue=Issue{id=1311351152067830064, tenantId=1146029695717560320, journalId=1205116964453384197, year='2026', volume='44', issue='1', pageStart='1', pageEnd='246', issueExtLink='null', onlineDate='null', pubDate='1769270400000', pubDateStr='2026-01-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1790580078378, creator='13701087609', updateTime=1790588459421, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1311386304772927717, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1311351152067830064, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1311386304772927718, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1311351152067830064, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=123, endPage=127, ext={EN=ArticleExt(id=1311351242719327078, articleId=1311351242568332133, tenantId=1146029695717560320, journalId=1205116964453384197, language=EN, title=Research on Optimization of Blasting Parameters Based on Improved Intelligent Optimization Algorithms, columnId=null, journalTitle=Water Resources and Power, columnName=null, runingTitle=null, highlight=null, articleAbstract=

In view of the problem that it is difficult to determine the optimization parameters of rock material yards and mine blasting in water conservancy projects, the traditional methods mainly rely on empirical formulas and experimental adjustments. To solve this issue, this study proposes a parameter optimization method that integrates an improved whale optimization algorithm (GWOA) with back propagation (BP) neural network. A GWOA-BP model was developed with optimization objectives focusing on explosive consumption per unit, borehole spacing, and row spacing. Comparative analysis was performed using measured blasting data of a certain project in Yunnan Province. The results indicate that the GWOA-BP model outperforms the conventional BP model in terms of prediction accuracy and stability, with error metrics significantly reduced. This research offers a novel technical approach for optimizing blasting parameters in simliar projects.

, authors=Chang LIU, Xiao-dong WANG, authorsList=Chang LIU, Xiao-dong WANG, authorCompany=null, correspAuthors=Xiao-dong WANG, 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, fund=null), CN=ArticleExt(id=1311351242987762535, articleId=1311351242568332133, tenantId=1146029695717560320, journalId=1205116964453384197, language=CN, title=基于改进智能优化算法的爆破参数优化研究, columnId=1207271182139736888, journalTitle=水电能源科学, columnName=水工结构、水工材料与水利工程施工, runingTitle=null, highlight=null, articleAbstract=

针对水利工程岩石料场及矿山爆破参数优化难以确定的问题，传统方法主要依赖经验公式和试验调整。为解决这一问题，提出了一种结合改进鲸鱼算法（GWOA）反向神经网络（BP）的参数优化方法。首先采用改进鲸鱼优化算法，构建了GWOA-BP模型，以炸药单耗、孔距和排距为优化目标，并结合云南省某工程实测爆破数据进行对比研究。结果显示，GWOA-BP模型在预测精度、稳定性方面均优于BP模型，误差指标显著降低。该研究成果为类似工程爆破参数优化提供了一种新技术手段。

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刘唱（1998-），男，硕士研究生，研究方向为机器学习与智能矿山，E-mail: 2771936758@qq.com

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刘唱（1998-），男，硕士研究生，研究方向为机器学习与智能矿山，E-mail: 2771936758@qq.com

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