Volume 15, Issue 3 (8-2025)
IJOCE 2025, 15(3): 419-445 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Paknahad M, Hosseini P, Kaveh A. PARAMETER-FREE STRUCTURAL OPTIMIZATION OF DOME TRUSSES: DEVELOPMENT AND APPLICATION OF THE SA_EVPS ALGORITHM WITH STATISTICAL LEARNING MECHANISMS. IJOCE 2025; 15 (3) :419-445
URL: http://ijoce.iust.ac.ir/article-1-647-en.html
URL: http://ijoce.iust.ac.ir/article-1-647-en.html
1- Faculty of Engineering, Mahallat Institute of Higher Education, Mahallat, Iran
2- School of Civil Engineering, Iran University of Science and Technology, Narmak, Tehran, Iran
2- School of Civil Engineering, Iran University of Science and Technology, Narmak, Tehran, Iran
Abstract: (678 Views)
This study presents the application of the Self-Adaptive Enhanced Vibrating Particle System (SA-EVPS) algorithm for large-scale dome truss optimization under frequency constraints. SA-EVPS incorporates self-adaptive parameter control, memory-based learning mechanisms, and statistical regeneration strategies to overcome limitations of traditional metaheuristic algorithms in structural optimization. The algorithm's performance is evaluated on three benchmark dome structures: (1) a 600-bar single-layer dome with 25 design variable groups, (2) an 1180-bar single-layer dome with 59 design variable groups, and (3) a 1410-bar double-layer dome with 47 design variable groups, all subject to natural frequency constraints. Comparative analysis against five state-of-the-art algorithms—Dynamic Particle Swarm Optimization (DPSO), Colliding Bodies Optimization (CBO), Enhanced Colliding Bodies Optimization (ECBO), Vibrating Particles System (VPS), and Enhanced Vibrating Particles System (EVPS)—demonstrates SA-EVPS's superior convergence characteristics and solution quality. Results show that SA-EVPS consistently achieves the lowest structural weights with remarkable stability across all test cases. The algorithm's self-adaptive mechanisms eliminate manual parameter tuning while the statistical regeneration mechanism prevents premature convergence in large-scale optimization problems. This research establishes SA-EVPS as a robust and efficient metaheuristic for frequency-constrained structural optimization of complex dome structures.
Keywords: SA_EVPS algorithm, dome truss optimization, frequency constraints, metaheuristic optimization, large-scale structural design, self-adaptive algorithms
Send email to the article author
| Rights and permissions | |
 | This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
