دوره 15، شماره 1 - ( 11-1403 )
جلد 15 شماره 1 صفحات 130-111 |
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Paknahd M, Hosseini P, Kaveh A, Hakim S. A SELF-ADAPTIVE ENHANCED VIBRATING PARTICLE SYSTEM ALGORITHM FOR STRUCTURAL OPTIMIZATION: APPLICATION TO ISCSO BENCHMARK PROBLEMS. IJOCE 2025; 15 (1) :111-130
URL: http://ijoce.iust.ac.ir/article-1-626-fa.html
URL: http://ijoce.iust.ac.ir/article-1-626-fa.html
A SELF-ADAPTIVE ENHANCED VIBRATING PARTICLE SYSTEM ALGORITHM FOR STRUCTURAL OPTIMIZATION: APPLICATION TO ISCSO BENCHMARK PROBLEMS. عنوان نشریه. 1403; 15 (1) :111-130
چکیده: (196 مشاهده)
Structural optimization plays a crucial role in engineering design, aiming to minimize weight and cost while satisfying performance constraints. This research presents a novel Self-Adaptive 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 Bang-Big 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.
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