将增强优化与有限元分析相结合,设计多重约束条件下的钢结构重量

IF 4.3 3区 工程技术 Q1 ENGINEERING, CIVIL
Dinh‐Nhat Truong, Jui-Sheng Chou
{"title":"将增强优化与有限元分析相结合,设计多重约束条件下的钢结构重量","authors":"Dinh‐Nhat Truong, Jui-Sheng Chou","doi":"10.3846/jcem.2023.20399","DOIUrl":null,"url":null,"abstract":"Real-world optimization problems are ubiquitous across scientific domains, and many engineering challenges can be reimagined as optimization problems with relative ease. Consequently, researchers have focused on developing optimizers to tackle these challenges. The Snake Optimizer (SO) is an effective tool for solving complex optimization problems, drawing inspiration from snake patterns. However, the original SO requires the specification of six specific parameters to operate efficiently. In response to this, enhanced snake optimizers, namely ESO1 and ESO2, were developed in this study. In contrast to the original SO, ESO1 and ESO2 rely on a single set of parameters determined through sensitivity analysis when solving mathematical functions. This streamlined approach simplifies the application of ESOs for users dealing with optimization problems. ESO1 employs a logistic map to initialize populations, while ESO2 further refines ESO1 by integrating a Lévy flight to simulate snake movements during food searches. These enhanced optimizers were compared against the standard SO and 12 other established optimization methods to assess their performance. ESO1 significantly outperforms other algorithms in 15, 16, 13, 15, 21, 16, 24, 16, 19, 18, 13, 15, and 22 out of 24 mathematical functions. Similarly, ESO2 outperforms them in 16, 17, 18, 22, 23, 23, 24, 20, 19, 20, 17, 22, and 23 functions. Moreover, ESO1 and ESO2 were applied to solve complex structural optimization problems, where they outperformed existing methods. Notably, ESO2 generated solutions that were, on average, 1.16%, 0.70%, 2.34%, 3.68%, and 6.71% lighter than those produced by SO, and 0.79%, 0.54%, 1.28%, 1.70%, and 1.60% lighter than those of ESO1 for respective problems. This study pioneers the mathematical evaluation of ESOs and their integration with the finite element method for structural weight design optimization, establishing ESO2 as an effective tool for solving engineering problems.","PeriodicalId":15524,"journal":{"name":"Journal of Civil Engineering and Management","volume":"13 8","pages":""},"PeriodicalIF":4.3000,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"INTEGRATING ENHANCED OPTIMIZATION WITH FINITE ELEMENT ANALYSIS FOR DESIGNING STEEL STRUCTURE WEIGHT UNDER MULTIPLE CONSTRAINTS\",\"authors\":\"Dinh‐Nhat Truong, Jui-Sheng Chou\",\"doi\":\"10.3846/jcem.2023.20399\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Real-world optimization problems are ubiquitous across scientific domains, and many engineering challenges can be reimagined as optimization problems with relative ease. Consequently, researchers have focused on developing optimizers to tackle these challenges. The Snake Optimizer (SO) is an effective tool for solving complex optimization problems, drawing inspiration from snake patterns. However, the original SO requires the specification of six specific parameters to operate efficiently. In response to this, enhanced snake optimizers, namely ESO1 and ESO2, were developed in this study. In contrast to the original SO, ESO1 and ESO2 rely on a single set of parameters determined through sensitivity analysis when solving mathematical functions. This streamlined approach simplifies the application of ESOs for users dealing with optimization problems. ESO1 employs a logistic map to initialize populations, while ESO2 further refines ESO1 by integrating a Lévy flight to simulate snake movements during food searches. These enhanced optimizers were compared against the standard SO and 12 other established optimization methods to assess their performance. ESO1 significantly outperforms other algorithms in 15, 16, 13, 15, 21, 16, 24, 16, 19, 18, 13, 15, and 22 out of 24 mathematical functions. Similarly, ESO2 outperforms them in 16, 17, 18, 22, 23, 23, 24, 20, 19, 20, 17, 22, and 23 functions. Moreover, ESO1 and ESO2 were applied to solve complex structural optimization problems, where they outperformed existing methods. Notably, ESO2 generated solutions that were, on average, 1.16%, 0.70%, 2.34%, 3.68%, and 6.71% lighter than those produced by SO, and 0.79%, 0.54%, 1.28%, 1.70%, and 1.60% lighter than those of ESO1 for respective problems. This study pioneers the mathematical evaluation of ESOs and their integration with the finite element method for structural weight design optimization, establishing ESO2 as an effective tool for solving engineering problems.\",\"PeriodicalId\":15524,\"journal\":{\"name\":\"Journal of Civil Engineering and Management\",\"volume\":\"13 8\",\"pages\":\"\"},\"PeriodicalIF\":4.3000,\"publicationDate\":\"2023-12-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Civil Engineering and Management\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.3846/jcem.2023.20399\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, CIVIL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Civil Engineering and Management","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.3846/jcem.2023.20399","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, CIVIL","Score":null,"Total":0}
引用次数: 0

摘要

现实世界中的优化问题在各个科学领域无处不在,许多工程挑战都可以相对容易地重新想象为优化问题。因此,研究人员一直致力于开发优化器来应对这些挑战。蛇形优化器(SO)从蛇形图案中汲取灵感,是解决复杂优化问题的有效工具。然而,最初的蛇形优化器需要指定六个特定参数才能有效运行。为此,本研究开发了增强型蛇形优化器,即 ESO1 和 ESO2。与最初的蛇形优化器相比,ESO1 和 ESO2 在求解数学函数时依赖于通过灵敏度分析确定的单组参数。这种精简的方法简化了处理优化问题的用户对 ESO 的应用。ESO1 采用逻辑图来初始化种群,而 ESO2 则通过整合莱维飞行来模拟蛇在寻找食物过程中的移动,从而进一步完善了 ESO1。我们将这些增强型优化器与标准 SO 和其他 12 种成熟的优化方法进行了比较,以评估它们的性能。在 24 个数学函数中,ESO1 在 15、16、13、15、21、16、24、16、19、18、13、15 和 22 个函数中的表现明显优于其他算法。同样,ESO2 在 16、17、18、22、23、23、24、20、19、20、17、22 和 23 个函数中的表现也优于其他算法。此外,ESO1 和 ESO2 还被应用于解决复杂的结构优化问题,它们在这方面的表现优于现有方法。值得注意的是,ESO2 生成的解决方案比 SO 生成的解决方案平均轻 1.16%、0.70%、2.34%、3.68% 和 6.71%,比 ESO1 生成的解决方案分别轻 0.79%、0.54%、1.28%、1.70% 和 1.60%。本研究开创性地对 ESO 进行了数学评估,并将其与有限元法相结合,用于结构重量设计优化,从而将 ESO2 确立为解决工程问题的有效工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
INTEGRATING ENHANCED OPTIMIZATION WITH FINITE ELEMENT ANALYSIS FOR DESIGNING STEEL STRUCTURE WEIGHT UNDER MULTIPLE CONSTRAINTS
Real-world optimization problems are ubiquitous across scientific domains, and many engineering challenges can be reimagined as optimization problems with relative ease. Consequently, researchers have focused on developing optimizers to tackle these challenges. The Snake Optimizer (SO) is an effective tool for solving complex optimization problems, drawing inspiration from snake patterns. However, the original SO requires the specification of six specific parameters to operate efficiently. In response to this, enhanced snake optimizers, namely ESO1 and ESO2, were developed in this study. In contrast to the original SO, ESO1 and ESO2 rely on a single set of parameters determined through sensitivity analysis when solving mathematical functions. This streamlined approach simplifies the application of ESOs for users dealing with optimization problems. ESO1 employs a logistic map to initialize populations, while ESO2 further refines ESO1 by integrating a Lévy flight to simulate snake movements during food searches. These enhanced optimizers were compared against the standard SO and 12 other established optimization methods to assess their performance. ESO1 significantly outperforms other algorithms in 15, 16, 13, 15, 21, 16, 24, 16, 19, 18, 13, 15, and 22 out of 24 mathematical functions. Similarly, ESO2 outperforms them in 16, 17, 18, 22, 23, 23, 24, 20, 19, 20, 17, 22, and 23 functions. Moreover, ESO1 and ESO2 were applied to solve complex structural optimization problems, where they outperformed existing methods. Notably, ESO2 generated solutions that were, on average, 1.16%, 0.70%, 2.34%, 3.68%, and 6.71% lighter than those produced by SO, and 0.79%, 0.54%, 1.28%, 1.70%, and 1.60% lighter than those of ESO1 for respective problems. This study pioneers the mathematical evaluation of ESOs and their integration with the finite element method for structural weight design optimization, establishing ESO2 as an effective tool for solving engineering problems.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
6.70
自引率
4.70%
发文量
0
审稿时长
1.7 months
期刊介绍: The Journal of Civil Engineering and Management is a peer-reviewed journal that provides an international forum for the dissemination of the latest original research, achievements and developments. We publish for researchers, designers, users and manufacturers in the different fields of civil engineering and management. The journal publishes original articles that present new information and reviews. Our objective is to provide essential information and new ideas to help improve civil engineering competency, efficiency and productivity in world markets. The Journal of Civil Engineering and Management publishes articles in the following fields: building materials and structures, structural mechanics and physics, geotechnical engineering, road and bridge engineering, urban engineering and economy, constructions technology, economy and management, information technologies in construction, fire protection, thermoinsulation and renovation of buildings, labour safety in construction.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信