Volume 16, Issue 3 (7-2026)
IJOCE 2026, 16(3): 441-466 |
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Fazli H. A CHAOTIC QUANTUM-INSPIRED PARTICLE SWARM OPTIMIZATION ALGORITHM FOR OPTIMAL DESIGN OF LARGE-SCALE STRUCTURES. IJOCE 2026; 16 (3) :441-466
URL: http://ijoce.iust.ac.ir/article-1-685-en.html
URL: http://ijoce.iust.ac.ir/article-1-685-en.html
Department of Civil Engineering, Payame Noor University, Tehran, Iran
Abstract: (98 Views)
Large-scale structural sizing optimization involves discrete design variables, nonlinear constraints, and repeated computationally expensive structural analyses. This study proposes a Chaotic Quantum-Inspired Particle Swarm Optimization (CQPSO) algorithm that integrates chaotic search mechanisms into QPSO to improve exploration, population diversity, and convergence. The proposed method is evaluated on a range of planar and spatial truss and frame benchmarks and compared with PSO, QPSO, chaotic PSO, CLPSO, differential evolution, and Grey Wolf Optimizer. Five chaotic maps are systematically examined within the CQPSO framework. The results show that CQPSO generally provides lower structural weight, greater robustness, and higher feasibility than the reference methods. The tent map provides the most favorable overall performance, with convergence and diversity analyses indicating an improved balance between exploration and exploitation. Statistical tests confirm that the observed improvements are significant for most comparisons, while computational results show that CQPSO reaches competitive designs with fewer structural evaluations and lower CPU time. The proposed approach therefore provides an effective and computationally efficient framework for discrete structural sizing optimization.
Keywords: Metaheuristic, Optimization, Chaotic, Quantum-Inspired, Particle Swarm, Large-scale structures
Type of Study: Research |
Subject:
Optimal design
Received: 2026/07/7 | Accepted: 2026/09/11 | Published: 2026/09/14
Received: 2026/07/7 | Accepted: 2026/09/11 | Published: 2026/09/14
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