PARAMETER-FREE STRUCTURAL OPTIMIZATION OF DOME TRUSSES: DEVELOPMENT AND APPLICATION OF THE SA_EVPS ALGORITHM WITH STATISTICAL LEARNING MECHANISMS

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: (۱) a ۶۰۰-bar single-layer dome with ۲۵ design variable groups, (۲) an ۱۱۸۰-bar single-layer dome with ۵۹ design variable groups, and (۳) a ۱۴۱۰-bar double-layer dome with ۴۷ 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