Optimal design method of temporary broadcasting tower structure based on improved genetic algorithm

doi:10.3785/j.issn.1008-973X.2025.03.004

Journal of ZheJiang University (Engineering Science)

2025, Vol. 59

Issue (3): 469-479 DOI: 10.3785/j.issn.1008-973X.2025.03.004

Optimal design method of temporary broadcasting tower structure based on improved genetic algorithm

Guohua XING(

),Yongjian LU,Pengyong MIAO*(

),Sijin CHEN

School of Civil Engineering, Chang’an University, Xi’an 710061, China

Download:

HTML

PDF(1957KB) HTML
Export: BibTeX | EndNote (RIS)

Abstract

A modular temporary broadcasting tower structure and its optimal design method were proposed to address the issues of excessive steel usage and low design refinement in temporary broadcast tower structures. The optimal design method combined parametric modeling and comprehensive optimization. Parametric finite element model was achieved using ANSYS to consider wind load fluctuations caused by structural design parameters during the optimization process. Five strategies including initial replacement, differentiation and adaptive genetics were used to enhance the search capability and convergence performance of the float-encoded genetic algorithm (FGA), leading to the proposal of an improved float-encoded genetic algorithm (IFGA). The IFGA was then utilized to comprehensively optimize the cross-section, shape, and members’ arrangement for the temporary broadcasting tower structure to achieve a lightweight plan. Validating in practical engineering revealed that the modular temporary broadcasting tower structure had excellent mechanical performance. In terms of structural lightweighting, the IFGA reduced the quality of steel used by 18.9% compared with the FGA, and it surpassed the school algorithm, whale algorithm and improved grey wolf algorithm in computational efficiency and optimization prowess. As a result, the proposed optimal design method was efficient and reliable, could save 33.2% of the quality of steel used compared with the original plan while ensuring structural safety.

Key words： structural optimization genetic algorithm finite element lightweight design temporary broadcasting tower

Received: 26 December 2023 Published: 10 March 2025

CLC:

TU 311.41

Fund: 陕西省重点研发计划“两链”融合企业（院所）联合重点专项(2022LL-JB-13).

Corresponding Authors: Pengyong MIAO E-mail: ghxing@chd.edu.cn;p.y.miao@outlook.com

	Service
	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Guohua XING
	Yongjian LU
	Pengyong MIAO
	Sijin CHEN

Cite this article:

Guohua XING,Yongjian LU,Pengyong MIAO,Sijin CHEN. Optimal design method of temporary broadcasting tower structure based on improved genetic algorithm. Journal of ZheJiang University (Engineering Science), 2025, 59(3): 469-479.

URL:

https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2025.03.004 OR https://www.zjujournals.com/eng/Y2025/V59/I3/469

基于改进遗传算法的临时转播塔结构优化设计方法

针对临时转播塔结构用钢质量较大且设计过程精细化程度低的问题，提出模块化临时转播塔结构及其优化设计方法. 所提设计方法将参数化建模与综合优化相结合，利用ANSYS建立结构参数化有限元模型，考虑结构设计参数在寻优过程中引起的风荷载变化；采用初始替换、差异化、自适应遗传等5种策略提升浮点数编码遗传算法(FGA)的搜索能力和收敛性能，提出改进浮点数编码遗传算法(IFGA)，综合优化临时转播塔结构的尺寸、形状和杆件布置形式，实现结构轻量化设计. 工程实例的应用表明，模块化临时转播塔结构的力学性能良好；在结构轻量化方面，IFGA算法相较于FGA算法优化结果在用钢质量上降低18.9%，相较于学校算法、鲸鱼算法及改进灰狼算法具有更高的计算效率和寻优能力；建议的优化设计方法高效、可靠，能在确保结构安全的前提下较初始方案在用钢质量上降低33.2%.

关键词： 结构优化, 遗传算法, 有限元, 轻量化设计, 临时转播塔

Fig.1 Typical standard section of tower

Fig.2 Modular temporary broadcasting tower structure

Tab.1 Structural design variables of temporary broadcasting tower

Tab.2 Comparison of finite element calculation results

Fig.3 Layout of diagonal members

Fig.4 Calculation process of fitness

Fig.5 Process of population initialization

Fig.6 Process of population differentiation

Fig.7 Calculation process of IFGA

Fig.8 Interactive interface of optimal design system

Fig.9 Initial population of genetic algorithms before and after improvement

Tab.3 Results and costs of GAs with different strategies

Fig.10 Evolutionary process of GAs with different strategies

Tab.4 Results and computing costs of different algorithms

Fig.11 Evolutionary process of different algorithms

Fig.12 Evolutionary process of structure mass

Tab.5 Mass savings between initial and optimized design

Fig.13 Standard sections of initial and optimized design

Fig.14 Evolutionary process of some constraint criteria

Fig.15 Equivalent stress of initial and optimized rods

Tab.6 Parameter comparison of initial and optimized design


[1]	HERNANDEZ-ESTRADA E, LASTRES-DANGUILLECOURT O, ROBLES-OCAMPO J B, et al Considerations for the structural analysis and design of wind turbine towers: a review[J]. Renewable and Sustainable Energy Reviews, 2021, 137: 110447 doi: 10.1016/j.rser.2020.110447

[2]	GKANTOU M, MARTINEZ-VAZQUEZ P, BANIOTOPOULOS C On the structural response of a tall hybrid onshore wind turbine tower[J]. Procedia Engineering, 2017, 199: 3200- 3205 doi: 10.1016/j.proeng.2017.09.535

[3]	CHEN J, YANG R, MA R, et al Design optimization of wind turbine tower with lattice-tubular hybrid structure using particle swarm algorithm[J]. The Structural Design of Tall and Special Buildings, 2008, 25: 743- 758 doi: 10.1002/tal.1281

[4]	郑瑞杰, 马人乐变截面构架式风力发电塔架 GA 优化[J]. 土木建筑与环境工程, 2009, 31 (6): 1- 6 ZHENG Ruijie, MA Renle GA optimization for variable cross section frame wind turbine[J]. Journal of Civil and Environmental Engineering, 2009, 31 (6): 1- 6

[5]	HU X, ZHANG Z, ZHU Z, et al. Structure Design and Optimization of Self-standing Modular Temporary Tower of 110kV Overhead Transmission Lines [C]// 5th International Conference on Machinery, Materials and Computing Technology . Beijing: Atlantis Press, 2017.

[6]	RENKAVIESKI C, PARPINELLI R S Meta-heuristic algorithms to truss optimization: Literature mapping and application[J]. Expert Systems with Applications, 2021, 182: 115197 doi: 10.1016/j.eswa.2021.115197

[7]	MORTAZAVI A, TOĞAN V, MOLOODPOOR M Solution of structural and mathematical optimization problems using a new hybrid swarm intelligence optimization algorithm[J]. Advances in Engineering Software, 2019, 127: 106- 123 doi: 10.1016/j.advengsoft.2018.11.004

[8]	SIVAKUMAR P, RAJARAMAN A, SAMUEL KNIGHT G, et al Object-oriented optimization approach using genetic algorithms for lattice towers[J]. Journal of Computing in Civil Engineering, 2004, 18 (2): 162- 171 doi: 10.1061/(ASCE)0887-3801(2004)18:2(162)

[9]	ARTAR M A comparative study on optimum design of multi-element truss structures[J]. Steel and Composite Structures, 2016, 22 (3): 521- 535 doi: 10.12989/scs.2016.22.3.521

[10]	KAVEH A, ZAKIAN P Improved GWO algorithm for optimal design of truss structures[J]. Engineering with Computers, 2018, 34 (4): 685- 707 doi: 10.1007/s00366-017-0567-1

[11]	JAWAD F K, OZTURK C, WANG D, et al Sizing and layout optimization of truss structures with artificial bee colony algorithm[J]. Structures, 2021, 30: 546- 559 doi: 10.1016/j.istruc.2021.01.016

[12]	COUCEIRO I, PARÍS J, MARTÍNEZ S, et al Structural optimization of lattice steel transmission towers[J]. Engineering Structures, 2016, 117: 274- 286 doi: 10.1016/j.engstruct.2016.03.005

[13]	COUCEIRO I, PARíS J, MARTíNEZ S, et al Computer software for analysis and design optimization of power transmission structures by simulated annealing and sensitivity analysis[J]. Engineering with Computers, 2021, 37 (4): 3649- 3663 doi: 10.1007/s00366-020-01022-x

[14]	HUANG X, LI B, ZHOU X, et al Geometric optimisation analysis of steel–concrete hybrid wind turbine towers[J]. Structures, 2022, 35: 1125- 1137 doi: 10.1016/j.istruc.2021.08.036

[15]	TSAVDARIDIS K D, NICOLAOU A, MISTRY A D, et al Topology optimisation of lattice telecommunication tower and performance-based design considering wind and ice loads[J]. Structures, 2020, 27: 2379- 2399 doi: 10.1016/j.istruc.2020.08.010

[16]	DE SOUZA R R, FADEL MIGUEL L F, LOPEZ R H, et al A procedure for the size, shape and topology optimization of transmission line tower structures[J]. Engineering Structures, 2016, 111: 162- 184 doi: 10.1016/j.engstruct.2015.12.005

[17]	DEGERTEKIN S O, LAMBERTI L, UGUR I B Sizing, layout and topology design optimization of truss structures using the Jaya algorithm[J]. Applied soft computing, 2018, 70: 903- 928 doi: 10.1016/j.asoc.2017.10.001

[18]	KHODZHAIEV M, REUTER U Structural optimization of transmission towers using a novel genetic algorithm approach with a variable length genome[J]. Engineering Structures, 2021, 240: 112306 doi: 10.1016/j.engstruct.2021.112306

[19]	金树, 任宗栋, 李宏男, 等输电塔结构离散变量优化设计方法[J]. 工程力学, 2016, 33 (11): 84- 94 JIN Shu, REN Zongdong, LI Hongnan, et al Discrete variable optimal design method of transmission tower structure[J]. Engineering Mechanics, 2016, 33 (11): 84- 94 doi: 10.6052/j.issn.1000-4750.2015.02.0102

[20]	LAGAROS N D, PLEVRIS V, KALLIORAS N A The mosaic of metaheuristic algorithms in structural optimization[J]. Archives of Computational Methods in Engineering, 2022, 29 (7): 5457- 5492 doi: 10.1007/s11831-022-09773-0

[21]	KAVEH A, HOSSEINI S M, ZAERREZA A Improved Shuffled Jaya algorithm for sizing optimization of skeletal structures with discrete variables[J]. Structures, 2021, 29: 107- 128 doi: 10.1016/j.istruc.2020.11.008

[22]	KAVEH A, KAMALINEJAD M, BIABANI HAMEDANI K, et al Quantum teaching-learning-based optimization algorithm for sizing optimization of skeletal structures with discrete variables[J]. Structures, 2021, 32: 1798- 1819 doi: 10.1016/j.istruc.2021.03.046

[23]	中华人民共和国住房和城乡建设部. 高耸结构设计标准: GB 50135—2019 [S]. 北京: 中国计划出版社, 2019.

[24]	中华人民共和国住房和城乡建设部. 建筑结构荷载规范: GB 50009—2012 [S]. 北京: 中国建筑工业出版社, 2012.

[25]	中国电力企业联合会. 110kV~750kV架空输电线路设计规范: GB 50545—2010 [S]. 北京: 中国计划出版社, 2010.

[26]	中华人民共和国住房和城乡建设部. 钢结构设计标准: GB 50017—2017 [S]. 北京: 中国建筑工业出版社, 2017.

[27]	GENTILS T, WANG L, KOLIOS A Integrated structural optimisation of offshore wind turbine support structures based on finite element analysis and genetic algorithm[J]. Applied Energy, 2017, 199: 187- 204 doi: 10.1016/j.apenergy.2017.05.009

[28]	陈俊岭, 何欣恒, 丛欧基于改进遗传算法的钢-混组合式风电机组塔架优化设计研究[J]. 太阳能学报, 2021, 42 (7): 359- 365 CHEN Junling, HE Xinheng, CONG Ou Design optimization of steel-concrete hybrid wind turbine tower based on improved genetic algorithm[J]. Acta Energiae Solaris Sinica, 2021, 42 (7): 359- 365

[29]	ALETI A, MOSER I A systematic literature review of adaptive parameter control methods for evolutionary algorithms[J]. ACM Computing Surveys, 2016, 49 (3): 1- 35

[30]	HERRERA F, LOZANO M Gradual distributed real-coded genetic algorithms[J]. IEEE transactions on evolutionary computation, 2000, 4 (1): 43- 63 doi: 10.1109/4235.843494

[31]	陈昆, 石国桢浮点数编码遗传算法变异概率的选取[J]. 武汉理工大学学报: 交通科学与工程版, 2001, 25 (4): 496- 499 CHEN Kun, SHI Guozhen Selection of mutation probability of floating-point number code genetic algorithm[J]. Journal of Wuhan University of Technology: Transportation Science and Engineering, 2001, 25 (4): 496- 499

[32]	MIRJALILI S, LEWIS A The whale optimization algorithm[J]. Advances in Engineering Software, 2016, 95: 51- 67 doi: 10.1016/j.advengsoft.2016.01.008

[1]	Yang WU,Lei XIAO,Weiyan WANG,Jinxin XU,Yuan DU,Boxin YANG,Qi AN. Finite element calculation method of contact stress between elevator safety gear brake block and guide rail[J]. Journal of ZheJiang University (Engineering Science), 2025, 59(1): 109-119.

[2]	Yunchen ZHU,Mingjun CHENG,Xinwen ZHENG,Peili CEN,Xiangshuo XI,Shan HUANG,Chen HUA,Hai HUANG. Fuzzy location selection of urban emergency facilities based on optimized non-dominated sorting genetic algorithm III[J]. Journal of ZheJiang University (Engineering Science), 2024, 58(9): 1832-1843.

[3]	Xingbiao ZHANG,Tao WANG,Sen YAO,Huawen YE,Lu WANG,Lunhua BAI. Dynamic response of cable fracture of long span road-rail cable-stayed suspension bridge[J]. Journal of ZheJiang University (Engineering Science), 2024, 58(9): 1874-1885.

[4]	Yali XUE,Hanyan LI,Quan OUYANG,Shan CUI,Jun HONG. Multi-UAVs collaborative task allocation based on genetic slime mould algorithm in battlefield environment[J]. Journal of ZheJiang University (Engineering Science), 2024, 58(8): 1748-1756.

[5]	Yifei LUO,Bin HU,Xin ZHAO,Zefeng WEN. Rolling contact and wear characteristics simulation analysis of scaled wheel-ring rail[J]. Journal of ZheJiang University (Engineering Science), 2024, 58(6): 1275-1284.

[6]	Jie ZHAO,Feng LIU,Ling XIA,Yifeng FAN. Passive shimming optimization method of MRI based on genetic algorithm-sequential quadratic programming[J]. Journal of ZheJiang University (Engineering Science), 2024, 58(6): 1305-1314.

[7]	Lifeng LI,Kun HOU,Deqiang ZOU,Hao PENG,Lingxiao LI. Optimization of section layout of steel plate composite beam with medium span[J]. Journal of ZheJiang University (Engineering Science), 2024, 58(3): 510-517.

[8]	Yan ZHAN,Jieya CHEN,Weiguang JIANG,Jiansha LU,Hongtao TANG,Xinyu SONG,Lili XU,Saimiao LIU. Multi-objective workshop material distribution method based on improved NSGA-Ⅱ[J]. Journal of ZheJiang University (Engineering Science), 2024, 58(12): 2510-2519.

[9]	Lifang CHEN,Huogen YANG,Zhichao CHEN,Jie YANG. Global path planning with integration of B-spline technique and genetic algorithm[J]. Journal of ZheJiang University (Engineering Science), 2024, 58(12): 2520-2530.

[10]	Yue’an WU,Changping DU,Rui YANG,Jiahao YU,Tianrui FANG,Yao ZHENG. Area coverage path planning for tilt-rotor unmanned aerial vehicle based on enhanced genetic algorithm[J]. Journal of ZheJiang University (Engineering Science), 2024, 58(10): 2031-2039.

[11]	Mi ZHOU,Zhao FENG,Pengli ZHANG,Wei QIN. Shaking table model test of cable-stayed bridge with large flow water pressure pipeline[J]. Journal of ZheJiang University (Engineering Science), 2024, 58(10): 2149-2161.

[12]	Hao ZHENG,Yi CAO,Shan WANG. Feeder bus route optimization of multiple subway stations considering station transfer[J]. Journal of ZheJiang University (Engineering Science), 2024, 58(10): 2162-2170.

[13]	Peng LIU,Qingchang LU,Han QIN,Xin CUI. Road network multi-stage disaster resistance optimization model and its application[J]. Journal of ZheJiang University (Engineering Science), 2024, 58(1): 96-108.

[14]	Jun-chao JIN,Lai-hong JING,Feng-wei YANG,Zhi-yu SONG,Peng-yang SHANG. Improvement of multi-step brittle-plastic approach[J]. Journal of ZheJiang University (Engineering Science), 2023, 57(9): 1706-1717.

[15]	Ling-mao WANG,Wei-jian ZHAO. Simulation on pullout behavior of mechanical anchorage reinforcing bars based on refined rib-scale modeling[J]. Journal of ZheJiang University (Engineering Science), 2023, 57(8): 1573-1584.

Viewed

Full text

Abstract

Cited

Shared

Discussed