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To enhance the safety and on-time performance of flight rerouting under thunderstorm conditions, the airspace was first discretized into grids. Considering meteorological avoidance zones, aircraft performance, and fuel consumption, an ARRPTC model incorporating multiple safety factors was established based on the aircraft's origin, destination, and departure time. The model determined both the aircraft's airborne holding time at the departure point and its optimal rerouting path, with the objective of minimizing the total flight time. Subsequently, a multi-strategy improved DBO (MSDBO) was designed according to the problem characteristics to improve convergence speed and accuracy while avoiding premature convergence of the algorithm. The performance of the proposed MSDBO was then compared with four other swarm intelligence algorithms by means of six benchmark test functions to verify the effectiveness of the improvement strategies. Finally, a case study based on an actual aircraft rerouting scenario under thunderstorm conditions was conducted. The results indicate that, compared with traditional models, the proposed ARRPTC model reduces the detour time and distance by 52.3% and 53.0%, respectively, while the total flight time and distance decrease by 16.5% and 14.3%, and fuel consumption is reduced by 13.6%. Compared with the other four swarm intelligence algorithms, MSDBO demonstrates faster convergence, stronger global exploration, and superior local exploitation capabilities. In the ARRPTC model, MSDBO can obtain more optimal flight trajectories, significantly improving the efficiency of flight rerouting. Furthermore, as the thunderstorm safety threshold and turbulence coefficient threshold decrease, the total rerouting distance tends to increase.

, authors=Ming WEI^{1, 2, 3}, Anming GAO¹, Xiaoxiao ZHANG², authorsList=Ming WEI, Anming GAO, Xiaoxiao ZHANG, authorCompany=null, correspAuthors=null, 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=1282037404664042091, articleId=1282037402231345767, tenantId=1146029695717560320, journalId=1146031787341344770, language=CN, title=求解航空器雷雨改航路径规划模型的改进蜣螂优化算法, columnId=1149733269727526997, journalTitle=中国安全科学学报, columnName=安全工程技术, runingTitle=null, highlight=null, articleAbstract=

为提高航班在雷雨天气下绕飞改航的安全性和正常率，首先，将空域网格化，考虑气象规避区、航空器性能、油耗等因素，根据航空器的起讫点和出发时间建立一种考虑多重安全因素的航空器雷雨绕飞改航路径规划（ARRPTC）模型，确定航空器在起点的空中盘旋等待时间及起讫点之间的绕飞路径，追求总飞行时间最少；其次，根据问题特征，设计求解该问题的多策略改进蜣螂优化算法（MSDBO），提高算法收敛速度与精度，避免算法陷入局部最优；然后，对比MSDBO与其他4种群体智能算法在6个基准测试函数上的性能，验证改进策略的有效性；最后，以某航空器真实雷雨绕飞为例进行仿真试验。结果表明：该模型相较于传统模型绕飞时间和距离分别减少52.3%和53.0%，总飞行时间和距离分别减少16.5%和14.3%，飞行油耗减少13.6%。与其他4种群体智能算法相比，MSDBO拥有更快的收敛速度、更好的全局探索和局部开发能力；MSDBO在航空器ARRPTC模型中能够求解出更好的飞行航迹，显著提高航班绕飞改航效率；随着雷雨安全阈值和颠簸系数阈值逐渐减小，航空器绕飞路径航程会有所增加。

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³Engineering Research Center for Intelligent Operation Control of Civil Aviation Airlines, China Academy of Civil Aviation Science and Technology, Beijing 100028, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1282037405486125691, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1282037402231345767, authorId=1282037405314159222, language=CN, stringName=魏明教授, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=^{1, 2, 3}, address=¹中国民航大学 空中交通管理学院，天津 300300
²北京航空航天大学 空地一体新航行系统技术全国重点实验室，北京 100191
³中国民航科学技术研究院 民航航空公司智慧运控工程技术研究中心，北京 100028, bio={"content":"

魏明（1984—），男，安徽芜湖人，博士，教授，主要从事智慧民航/交通大数据、复杂系统建模与优化智能交通等方面的研究。E-mail: mwei@cauc.edu.cn。

"}, bioImg=null, bioContent=

魏明（1984—），男，安徽芜湖人，博士，教授，主要从事智慧民航/交通大数据、复杂系统建模与优化智能交通等方面的研究。E-mail: mwei@cauc.edu.cn。

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