[1]张泽郴,闫磊,任伟.基于改进粒子群优化算法的先张折线梁参数优化[J].长安大学学报(自然科学版),2025,45(6):143-153.
　ZHANG Ze-chen,YAN Lei,REN Wei.Parameter optimization of pre-tensioned broken-line beam based on improved particle swarm optimization algorithm[J].Journal of Chang’an University (Natural Science Edition),2025,45(6):143-153.
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基于改进粒子群优化算法的先张折线梁参数优化()
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长安大学学报（自然科学版）[ISSN:1006-6977/CN:61-1281/TN]

卷:
第45卷
期数:
2025年6期
页码:
143-153
栏目:
桥梁智能运维与防灾减灾
出版日期:
2025-11-30

文章信息/Info

Title:
Parameter optimization of pre-tensioned broken-line beam based on improved particle swarm optimization algorithm
文章编号:
1671-8879(2025)06-0143-11
作者:
张泽郴; 闫磊; 任伟
(长安大学 公路学院,陕西 西安 710064)
Author(s):
ZHANG Ze-chen;  YAN Lei;  REN Wei
(School of Highway, Chang'an University, Xi'an 710064, Shaanxi, China)
关键词:
桥梁工程;  改进粒子群优化算法;  粒子能力分级;  反对粒子;  设计参数优化
Keywords:
bridge engineering;  improved particle swarm optimization algorithm;  particle capability grading;  opposition particle;  design parameter optimization
分类号:
U448.218
文献标志码:
A
摘要:
为解决桥梁设计中多变量、多约束条件下最优设计参数的取值问题,提出了改进粒子群优化(IPSO)算法,优化了先张折线束工字组合梁的设计参数; 基于生态位分化理论建立了粒子能力分级机制,依据第十人理论提出了反对粒子策略,将二者与PSO算法进行融合,构建了IPSO算法; 利用4种基准测试函数对该算法进行了独立试验,验证了IPSO算法的寻优能力与收敛效率; 利用IPSO算法优化了一片35 m先张折线束工字组合梁的设计参数,并通过定值与可靠度校验证明了该算法的可行性。分析结果表明:单峰函数测试中,IPSO算法的平均收敛率较PSO算法提高13.4%以上,且迭代结果平均值至少降低99.4%,证明粒子能力分级机制可显著提升算法的收敛效率与局部寻优能力; 多峰函数测试中,IPSO算法的平均收敛率较PSO算法提高15.0%以上,部分粒子的反向搜索计算使得迭代结果平均值至少降低76.2%,标准差至少降低73.9%,说明迭代结果更逼近于目标值且收敛效果更优,故反对粒子策略可显著提升算法的全局寻优能力; 优化后先张折线束工字组合梁桥面板高度可减少25.0%,梁高可减少9.0%,预应力钢束可减少5.6%,单片梁的材料成本可降低9.1%,且设计参数仍满足规范要求,说明提出的IPSO算法可信、可行。研究成果可为同类结构的设计优化提供参考。
Abstract:
To address the problem of optimal design parameter selection under multi-variable and multi-constraint conditions in bridge design, an improved particle swarm optimization(IPSO)algorithm was proposed to optimize the design parameters of a pre-tensioned broken-line I-shape composite beam. A particle capability grading mechanism was established based on the ecological niche differentiation theory. According to the tenth man rule, an opposition particle strategy was proposed. The two strategies were integrated with the PSO algorithm to construct the IPSO algorithm. Four benchmark test functions were employed to conduct independent tests to verify the optimization ability and convergence efficiency of the IPSO algorithm. The IPSO algorithm was then used to optimize the design parameters of a 35 m pre-tensioned broken-line I-shape composite beam, and its feasibility was validated through fixed-value and reliability verification. The analysis results show that in the unimodal function test, the average convergence rate of IPSO algorithm improves by more than 13.4% compared with the PSO algorithm, and the average value of iteration results reduces by at least 99.4%. This proves that the particle capability grading mechanism can significantly enhance the convergence efficiency and local optimization ability of the algorithm. In the multimodal function test, the average convergence rate of IPSO algorithm improves by more than 15.0% compared with the PSO algorithm, the reverse search computation of some particles makes the average value of iteration results decrease by at least 76.2% and the standard deviation decrease by at least 73.9%, indicating that the iteration results are closer to the target values and the convergence is better. Therefore, the opposition particle strategy can significantly enhance the global optimization ability of the algorithm. After optimization, the deck height of the pre-tensioned broken-line I-shape composite beam reduces by 25.0%, the beam height reduces by 9.0%, the prestressed tendons reduce by 5.6%, and the material cost per beam reduces by 9.1%, while all design parameters still meet the specification requirements. This confirms that the proposed IPSO algorithm is reliable and feasible. The research results can provide a reference for the design optimization of similar structures.9 tabs, 8 figs, 33 refs.

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备注/Memo

备注/Memo:
收稿日期:2025-05-25
基金项目:国家重点研发计划项目(2024YFB2605004)
作者简介:张泽郴(1996-),男,山西运城人,E-mail:237319816@qq.com。
通信作者:闫 磊(1979-),男,山西运城人,副教授,工学博士,E-mail:yanlei@chd.edu.cn。
更新日期/Last Update: 2025-12-20