Abstract
In this paper, a new modified particle swarm optimization (PSO) algorithm is utilized in optimal design of large-span prestressed concrete slabs. The modification is performed by adding some probabilistic coefficients to the velocity of particles, and it is called probabilistic particle swarm optimization (PPSO). These coefficients provide simultaneous exploration and exploitation for the algorithm, and decrease dependency of PSO on its constants. To examine the robustness of the enhanced algorithm, the model of a large-span prestressed concrete slab is generated using SAP2000 and is linked to the considered metaheuristic code to provide an optimal design. Results of PPSO are compared to those of the PSO and harmony search. A better performance of the PPSO is shown compared to the metaheuristics considered. PPSO is shown to converge faster and results in lower weight. Furthermore, a parametric study shows that the PPSO is less sensitive to the inertia weight.
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Kaveh, A., Talaei, A.S. & Nasrollahi, A. Application of Probabilistic Particle Swarm in Optimal Design of Large-Span Prestressed Concrete Slabs. Iran. J. Sci. Technol.Trans. Civ. Eng. 40, 33–40 (2016). https://doi.org/10.1007/s40996-016-0005-4
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DOI: https://doi.org/10.1007/s40996-016-0005-4