Showing 2 results for Large-Scale Structures
A. Zaerreza, P. Hassanvand, S. R. Nabavian,
Volume 16, Issue 1 (1-2026)
Abstract
The VPS-SRM algorithm is an enhanced metaheuristic approach developed for structural optimization. While it demonstrates robust performance in structural design, its efficiency remains subject to improvement, especially when dealing with large-scale structural optimization problems. To address this, the present study introduces improved versions of the VPS-SRM by incorporating chaotic maps. The performance of these chaotic-based variants was evaluated through the optimization of large-scale structural problems, including a 3-bay 15-story frame, 520-bar double-layer grid, and 800-bar double-layer grid. The results indicate that the chaotic versions significantly outperform the original algorithm, providing superior structural designs with higher precision and enhanced statistical results. Statistical analysis via the Kruskal-Wallis test further confirms that the chaotic variants offer a substantial improvement over the standard VPS-SRM.
H. Fazli,
Volume 16, Issue 3 (7-2026)
Abstract
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.
