A Self-Adaptive Enhanced Vibrating Particle System Algorithm For Structural Optimization: Application To Iscso Benchmark Problems
Structural optimization plays a crucial role in engineering design, aiming to minimize weight and cost while satisfying performance constraints. This research presents a novel SelfAdaptive Enhanced Vibrating Particle System (SA-EVPS) algorithm that automatically adjusts algorithm parameters to impro...
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2025
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| Online Access: | http://eprints.uthm.edu.my/12633/1/J19602_0feb238284a1f1a3b3458df5ff40a479.pdf http://eprints.uthm.edu.my/12633/ |
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| author | Paknahd, M Hosseini, P Kaveh, A Hakim, S.J.S |
| author_facet | Paknahd, M Hosseini, P Kaveh, A Hakim, S.J.S |
| author_sort | Paknahd, M |
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| content_provider | Universiti Tun Hussein Onn Malaysia |
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| continent | Asia |
| country | Malaysia |
| description | Structural optimization plays a crucial role in engineering design, aiming to minimize weight and cost while satisfying performance constraints. This research presents a novel SelfAdaptive Enhanced Vibrating Particle System (SA-EVPS) algorithm that automatically adjusts algorithm parameters to improve optimization performance. The algorithm is applied to two challenging examples from the International Student Competition in Structural Optimization (ISCSO) benchmark suite: the 314-member truss structure (ISCSO_2018) and the 345-member truss structure (ISCSO_2021). Results demonstrate that SA-EVPS achieves significantly better solutions compared to previous studies using the Exponential Big BangBig Crunch (EBB-BC) algorithm. For ISCSO_2018, SA-EVPS achieved a minimum weight of 16543.57 kg compared to 17934.3 kg for the best EBB-BC variant—a 7.75% improvement. Similarly, for ISCSO_2021, SA-EVPS achieved 4292.71 kg versus 4399.0 kg for the best EBB-BC variant—a 2.42% improvement. The proposed algorithm also demonstrates superior convergence behavior and solution consistency, with coefficients of variation of 3.13% and 1.21% for the two benchmark problems, compared to 12.5% and 2.4% for the best EBB-BC variant. These results highlight the effectiveness of the SA-EVPS
algorithm for solving complex structural optimization problems and demonstrate its potential for engineering applications. |
| format | Article |
| id | my.uthm.eprints-12633 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | en |
| publishDate | 2025 |
| record_format | eprints |
| spelling | my.uthm.eprints-126332025-06-05T01:11:27Z http://eprints.uthm.edu.my/12633/ A Self-Adaptive Enhanced Vibrating Particle System Algorithm For Structural Optimization: Application To Iscso Benchmark Problems Paknahd, M Hosseini, P Kaveh, A Hakim, S.J.S TA Engineering (General). Civil engineering (General) Structural optimization plays a crucial role in engineering design, aiming to minimize weight and cost while satisfying performance constraints. This research presents a novel SelfAdaptive Enhanced Vibrating Particle System (SA-EVPS) algorithm that automatically adjusts algorithm parameters to improve optimization performance. The algorithm is applied to two challenging examples from the International Student Competition in Structural Optimization (ISCSO) benchmark suite: the 314-member truss structure (ISCSO_2018) and the 345-member truss structure (ISCSO_2021). Results demonstrate that SA-EVPS achieves significantly better solutions compared to previous studies using the Exponential Big BangBig Crunch (EBB-BC) algorithm. For ISCSO_2018, SA-EVPS achieved a minimum weight of 16543.57 kg compared to 17934.3 kg for the best EBB-BC variant—a 7.75% improvement. Similarly, for ISCSO_2021, SA-EVPS achieved 4292.71 kg versus 4399.0 kg for the best EBB-BC variant—a 2.42% improvement. The proposed algorithm also demonstrates superior convergence behavior and solution consistency, with coefficients of variation of 3.13% and 1.21% for the two benchmark problems, compared to 12.5% and 2.4% for the best EBB-BC variant. These results highlight the effectiveness of the SA-EVPS algorithm for solving complex structural optimization problems and demonstrate its potential for engineering applications. 2025 Article PeerReviewed text en http://eprints.uthm.edu.my/12633/1/J19602_0feb238284a1f1a3b3458df5ff40a479.pdf Paknahd, M and Hosseini, P and Kaveh, A and Hakim, S.J.S (2025) A Self-Adaptive Enhanced Vibrating Particle System Algorithm For Structural Optimization: Application To Iscso Benchmark Problems. International Journal Of Optimization In Civil Engineering, 15 (1). pp. 111-130. |
| spellingShingle | TA Engineering (General). Civil engineering (General) Paknahd, M Hosseini, P Kaveh, A Hakim, S.J.S A Self-Adaptive Enhanced Vibrating Particle System Algorithm For Structural Optimization: Application To Iscso Benchmark Problems |
| title | A Self-Adaptive Enhanced Vibrating Particle System Algorithm For Structural Optimization: Application To Iscso Benchmark Problems |
| title_full | A Self-Adaptive Enhanced Vibrating Particle System Algorithm For Structural Optimization: Application To Iscso Benchmark Problems |
| title_fullStr | A Self-Adaptive Enhanced Vibrating Particle System Algorithm For Structural Optimization: Application To Iscso Benchmark Problems |
| title_full_unstemmed | A Self-Adaptive Enhanced Vibrating Particle System Algorithm For Structural Optimization: Application To Iscso Benchmark Problems |
| title_short | A Self-Adaptive Enhanced Vibrating Particle System Algorithm For Structural Optimization: Application To Iscso Benchmark Problems |
| title_sort | self-adaptive enhanced vibrating particle system algorithm for structural optimization: application to iscso benchmark problems |
| topic | TA Engineering (General). Civil engineering (General) |
| url | http://eprints.uthm.edu.my/12633/1/J19602_0feb238284a1f1a3b3458df5ff40a479.pdf http://eprints.uthm.edu.my/12633/ |
| url_provider | http://eprints.uthm.edu.my/ |