en Optimal design Optimal design پژوهشي Research <span style="font-size:12pt"><span style="line-height:normal"><span style="unicode-bidi:embed"><span new="" roman="" style="font-family:" times=""><span style="font-size:11.5pt">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&#39;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&mdash;Dynamic Particle Swarm Optimization (DPSO), Colliding Bodies Optimization (CBO), Enhanced Colliding Bodies Optimization (ECBO), Vibrating Particles System (VPS), and Enhanced Vibrating Particles System (EVPS)&mdash;demonstrates SA-EVPS&#39;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&#39;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.</span></span></span></span></span> SA_EVPS algorithm, dome truss optimization, frequency constraints, metaheuristic optimization, large-scale structural design, self-adaptive algorithms 419 445 http://ijoce.iust.ac.ir/browse.php?a_code=A-10-6463-33&slc_lang=en&sid=1 M. Paknahad 180031947532846002853 180031947532846002853 No Faculty of Engineering, Mahallat Institute of Higher Education, Mahallat, Iran P. Hosseini p.hosseini@mahallat.ac.ir 180031947532846002854 180031947532846002854 Yes Faculty of Engineering, Mahallat Institute of Higher Education, Mahallat, Iran A. Kaveh alikaveh@iust.ac.ir 180031947532846002855 180031947532846002855 No School of Civil Engineering, Iran University of Science and Technology, Narmak, Tehran, Iran