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 effect...
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| Format: | Article |
| Language: | English |
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Vilnius Gediminas Technical University
2023-12-01
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| Series: | Journal of Civil Engineering and Management |
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| Online Access: | https://mma.vgtu.lt/index.php/JCEM/article/view/20399 |
| _version_ | 1827573231488335872 |
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| author | Dinh-Nhat Truong Jui-Sheng Chou |
| author_facet | Dinh-Nhat Truong Jui-Sheng Chou |
| author_sort | Dinh-Nhat Truong |
| collection | DOAJ |
| description |
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.
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| first_indexed | 2024-03-08T20:15:21Z |
| format | Article |
| id | doaj.art-ba9697315db0492f84117f09972ef90c |
| institution | Directory Open Access Journal |
| issn | 1392-3730 1822-3605 |
| language | English |
| last_indexed | 2024-03-08T20:15:21Z |
| publishDate | 2023-12-01 |
| publisher | Vilnius Gediminas Technical University |
| record_format | Article |
| series | Journal of Civil Engineering and Management |
| spelling | doaj.art-ba9697315db0492f84117f09972ef90c2023-12-22T16:22:48ZengVilnius Gediminas Technical UniversityJournal of Civil Engineering and Management1392-37301822-36052023-12-0129810.3846/jcem.2023.20399Integrating enhanced optimization with finite element analysis for designing steel structure weight under multiple constraintsDinh-Nhat Truong0Jui-Sheng Chou1Department of Civil and Construction Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan; Department of Civil Engineering, University of Architecture Ho Chi Minh City, Ho Chi Minh City, Viet NamDepartment of Civil and Construction Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan 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. https://mma.vgtu.lt/index.php/JCEM/article/view/20399steel structural designfinite element analysismetaheuristic algorithmenhanced optimizerbenchmark functions |
| spellingShingle | Dinh-Nhat Truong Jui-Sheng Chou Integrating enhanced optimization with finite element analysis for designing steel structure weight under multiple constraints Journal of Civil Engineering and Management steel structural design finite element analysis metaheuristic algorithm enhanced optimizer benchmark functions |
| title | Integrating enhanced optimization with finite element analysis for designing steel structure weight under multiple constraints |
| title_full | Integrating enhanced optimization with finite element analysis for designing steel structure weight under multiple constraints |
| title_fullStr | Integrating enhanced optimization with finite element analysis for designing steel structure weight under multiple constraints |
| title_full_unstemmed | Integrating enhanced optimization with finite element analysis for designing steel structure weight under multiple constraints |
| title_short | Integrating enhanced optimization with finite element analysis for designing steel structure weight under multiple constraints |
| title_sort | integrating enhanced optimization with finite element analysis for designing steel structure weight under multiple constraints |
| topic | steel structural design finite element analysis metaheuristic algorithm enhanced optimizer benchmark functions |
| url | https://mma.vgtu.lt/index.php/JCEM/article/view/20399 |
| work_keys_str_mv | AT dinhnhattruong integratingenhancedoptimizationwithfiniteelementanalysisfordesigningsteelstructureweightundermultipleconstraints AT juishengchou integratingenhancedoptimizationwithfiniteelementanalysisfordesigningsteelstructureweightundermultipleconstraints |