Journal of Changjiang River Scientific Research Institute ›› 2025, Vol. 42 ›› Issue (1): 201-207.DOI: 10.11988/ckyyb.20230923
• Hydraulic Structure and Material • Previous Articles Next Articles
HAN Yi-feng(), HU Jian-ke, WANG Jing-kun
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Abstract:
Addressing the challenge of obtaining global optimal solutions for steel gate optimization problems, this study introduces an active target particle swarm optimization (APSO) algorithm for the optimization design of emersed plane steel gates. APSO incorporates an active target individual into the conventional particle swarm optimization (PSO) population and integrates it into the algorithm’s iterative update mechanism. This enhancement bolsters the algorithm’s capability to escape local optima and enhances its global optimization performance. Furthermore, the APSO algorithm employs a comprehensive learning factor in place of multiple individual learning factors used in traditional PSO, thereby improving the convergence rate and stability. Under constraints imposed by steel structure strength requirements, the APSO algorithm optimizes key structural parameters, including those of the main beam, side columns, panel, and secondary beams, with the objective of minimizing the total weight of the gate. After optimization, finite element analysis (FEA) is conducted using ABAQUS to verify the strength integrity of the main beam based on the optimized design parameters. Findings indicate that the APSO algorithm effectively optimizes the design of emersed plane steel gates, yielding improved structural dimensions. Specifically, the optimized gate design achieves a 15.38% reduction in total weight compared to previous literature examples, while stringent strength checks confirm compliance with allowable stress limits.
Key words: emersed plane steel gate, allowable stress, active target particle swarm optimization algorithm, strength checking, optimization design
CLC Number:
TV663闸门
HAN Yi-feng, HU Jian-ke, WANG Jing-kun. Optimization of Emersed Plane Steel Gate Based on Active Target Particle Swarm Optimization Algorithm[J]. Journal of Changjiang River Scientific Research Institute, 2025, 42(1): 201-207.
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URL: http://ckyyb.crsri.cn/EN/10.11988/ckyyb.20230923
http://ckyyb.crsri.cn/EN/Y2025/V42/I1/201
Fig.1 Sectional view of the panel, main beam and side column
Table 1 Control variables
Fig.2 Flow chart of the algorithm
Table 2 Upper and lower limits of variables
Table 3 Comparison of optimization results
Fig.3 Comparison of convergence curves
Table 4 Statistics of optimization results
Fig.4 Statistical comparison of 20 calculation results
Fig.5 Finite element analysis model of emersed plane steel gate
Fig.6 Stress contours of lower and upper main beams