Structural optimization of different truss designs using two archive multi objective crystal structure optimization algorithm
Abstract Optimizing a multi-objective structure is a challenging design problem that requires handling several competing goals and constraints. Despite their success in resolving such issues, metaheuristics can be difficult to apply due to their stochastic nature and restrictions. This work proposes...
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Nature Portfolio
2025-04-01
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| Online Access: | https://doi.org/10.1038/s41598-025-97133-w |
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| author | Pranav Mehta Ghanshyam G. Tejani Seyed Jalaleddin Mousavirad |
| author_facet | Pranav Mehta Ghanshyam G. Tejani Seyed Jalaleddin Mousavirad |
| author_sort | Pranav Mehta |
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| description | Abstract Optimizing a multi-objective structure is a challenging design problem that requires handling several competing goals and constraints. Despite their success in resolving such issues, metaheuristics can be difficult to apply due to their stochastic nature and restrictions. This work proposes the multi-objective crystal structure optimizer (MOCRY), a potent and effective optimizer, to address this problem. The MOCRY algorithm, also known as MOCRY2arc, is built on a two-archive idea centered on diversity and convergence, respectively. The efficacy of MOCRY2arc in solving five typical planar and spatial real-world structure optimization issues was assessed. Because of these problems, safety and size limits were put on discrete cross-sectional regions and component stress. At the same time, different goals were being pursued, such as making nodal points bend more and reducing the mass of trusses. Four recognized standard evaluators—Hypervolume (HV), Generational-Inverted Generational Distance (GD, IGD), Spacing to Extent Metrics (STE), convergence, and diversity plots—were utilized to compare the results with those of nine sophisticated optimization techniques, including MOCRY and NSGA-II. Moreover, the Friedman rank test and comparison analysis showed that MOCRY2arc performed better at resolving big structure optimization issues in a shorter amount of computing time. In addition to identifying and realizing effective Pareto-optimal sets, the recommended method produced strong variety and convergence in the objective and choice spaces. As a result, MOCRY2arc may be a useful tool for handling challenging multi-objective structure optimization issues. |
| format | Article |
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| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
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| spelling | doaj-art-d7a4ee1ed44c4a48aac06c0d81c8bd282025-08-20T01:59:56ZengNature PortfolioScientific Reports2045-23222025-04-0115113510.1038/s41598-025-97133-wStructural optimization of different truss designs using two archive multi objective crystal structure optimization algorithmPranav Mehta0Ghanshyam G. Tejani1Seyed Jalaleddin Mousavirad2Department of Mechanical Engineering, Dharmsinh Desai UniversityDepartment of Research Analytics, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha UniversityDepartment of Computer and Electrical Engineering, Mid Sweden UniversityAbstract Optimizing a multi-objective structure is a challenging design problem that requires handling several competing goals and constraints. Despite their success in resolving such issues, metaheuristics can be difficult to apply due to their stochastic nature and restrictions. This work proposes the multi-objective crystal structure optimizer (MOCRY), a potent and effective optimizer, to address this problem. The MOCRY algorithm, also known as MOCRY2arc, is built on a two-archive idea centered on diversity and convergence, respectively. The efficacy of MOCRY2arc in solving five typical planar and spatial real-world structure optimization issues was assessed. Because of these problems, safety and size limits were put on discrete cross-sectional regions and component stress. At the same time, different goals were being pursued, such as making nodal points bend more and reducing the mass of trusses. Four recognized standard evaluators—Hypervolume (HV), Generational-Inverted Generational Distance (GD, IGD), Spacing to Extent Metrics (STE), convergence, and diversity plots—were utilized to compare the results with those of nine sophisticated optimization techniques, including MOCRY and NSGA-II. Moreover, the Friedman rank test and comparison analysis showed that MOCRY2arc performed better at resolving big structure optimization issues in a shorter amount of computing time. In addition to identifying and realizing effective Pareto-optimal sets, the recommended method produced strong variety and convergence in the objective and choice spaces. As a result, MOCRY2arc may be a useful tool for handling challenging multi-objective structure optimization issues.https://doi.org/10.1038/s41598-025-97133-wMulti-objective optimizers2-archivesTruss optimizationPareto-fronts |
| spellingShingle | Pranav Mehta Ghanshyam G. Tejani Seyed Jalaleddin Mousavirad Structural optimization of different truss designs using two archive multi objective crystal structure optimization algorithm Scientific Reports Multi-objective optimizers 2-archives Truss optimization Pareto-fronts |
| title | Structural optimization of different truss designs using two archive multi objective crystal structure optimization algorithm |
| title_full | Structural optimization of different truss designs using two archive multi objective crystal structure optimization algorithm |
| title_fullStr | Structural optimization of different truss designs using two archive multi objective crystal structure optimization algorithm |
| title_full_unstemmed | Structural optimization of different truss designs using two archive multi objective crystal structure optimization algorithm |
| title_short | Structural optimization of different truss designs using two archive multi objective crystal structure optimization algorithm |
| title_sort | structural optimization of different truss designs using two archive multi objective crystal structure optimization algorithm |
| topic | Multi-objective optimizers 2-archives Truss optimization Pareto-fronts |
| url | https://doi.org/10.1038/s41598-025-97133-w |
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