Volume 16, Issue 1 (1-2026)
IJOCE 2026, 16(1): 1-29 |
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Ravan M, Rahami H, Shokoohfar M R. PROBABILISTIC GUIDE-SELECTION PARTICLE SWARM OPTIMIZATION. IJOCE 2026; 16 (1) :1-29
URL: http://ijoce.iust.ac.ir/article-1-660-en.html
URL: http://ijoce.iust.ac.ir/article-1-660-en.html
1- School of Civil Engineering, College of Engineering, University of Tehran, Tehran, Iran
2- School of Engineering Science, College of Engineering, University of Tehran, Tehran, Iran
2- School of Engineering Science, College of Engineering, University of Tehran, Tehran, Iran
Abstract: (415 Views)
Optimization is a key tool for solving complex engineering problems. This research introduces a novel particle swarm optimization algorithm in which all particles have a probability of being selected as guide particles, while the likelihood of each particle influencing others is determined proportionally to its performance. In other words, unlike the classical PSO algorithm where only the best particle is chosen as the fixed guide in each iteration, every particle can independently select its own guide based on the performance of other particles. This approach appears to prevent premature convergence of particles and enhance the exploration capability of the algorithm. Additionally, a parameter has been defined and investigated in this algorithm to adjust the ratio of exploration to exploitation power, which can be initialized according to the complexity type of the problem. The performance of the proposed algorithm was first evaluated using a set of benchmark mathematical functions, which confirmed the high accuracy of the algorithm in finding optimal solutions. Then, several truss design problems were examined as real structural case studies, and the obtained results indicate that the proposed algorithm exhibits suitable and acceptable performance compared with other well-known algorithms.
Keywords: Optimization, Structural optimization, Computational efficiency, Particle swarm optimization, Metaheuristic Search Algorithms
Type of Study: Research |
Subject:
Optimal design
Received: 2025/11/13 | Accepted: 2026/01/3 | Published: 2026/01/7
Received: 2025/11/13 | Accepted: 2026/01/3 | Published: 2026/01/7
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