Volume 43 Issue 10
Oct.  2022
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TAO Ran, ZHOU Huanlin, MENG Zeng, YANG Xiaomeng. Optimization Design of Holding Poles Based on the Response Surface Methodology and the Improved Arithmetic Optimization Algorithm[J]. Applied Mathematics and Mechanics, 2022, 43(10): 1113-1122. doi: 10.21656/1000-0887.420318
Citation: TAO Ran, ZHOU Huanlin, MENG Zeng, YANG Xiaomeng. Optimization Design of Holding Poles Based on the Response Surface Methodology and the Improved Arithmetic Optimization Algorithm[J]. Applied Mathematics and Mechanics, 2022, 43(10): 1113-1122. doi: 10.21656/1000-0887.420318

Optimization Design of Holding Poles Based on the Response Surface Methodology and the Improved Arithmetic Optimization Algorithm

doi: 10.21656/1000-0887.420318
  • Received Date: 2021-10-25
  • Rev Recd Date: 2022-05-25
  • Available Online: 2022-05-26
  • Publish Date: 2022-10-31
  • The computation consumption of finite element analysis for structural optimization design of holding poles is large, and it is difficult to determine the feasible region. The response surface method (RSM) was used to simulate the real response of the holding pole, and an improved arithmetic optimization algorithm (IAOA) was proposed to optimize the holding pole. The fractional-order calculus was introduced into the arithmetic optimization algorithm (AOA) to improve the exploitation ability of the AOA. The Latin hypercube sampling was applied to select the test samples of each member of the holding pole, and the least square method was employed to analyze the sample points. Then, the 2nd-order response surface surrogate model for the stress and displacement of the holding pole on the cross-sectional sizes of each member was established. An optimization model was constructed with the minimum mass as the optimization objective and the allowable stress and displacement as constraints, and the IAOA was implemented to solve the model. The results show that, the 2nd-order response surface model can accurately predict the response value of the holding pole. The solution accuracy of the IAOA is significantly improved. The surrogate model can greatly decrease the calculation cost of the finite element analysis. The mass of the holding pole is reduced by 8.2% after optimization. The RSM and the IAOA can be combined to solve the optimization design problem of large spatial truss structures effectively.

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  • [1]
    国家能源局. 架空输电线路施工抱杆通用技术条件及试验方法: DL/T 319—2018[S]. 北京: 中国电力出版社, 2018.

    National Energy Administration. General technical conditions and test methods for holding pole of overhead transmission line construction: DL/T 319—2018[S]. Beijing: China Electric Power Press, 2018. (in Chinese)
    [2]
    池沛, 董军, 夏志远, 等. 架空输电线路摇臂抱杆应力状态分析及试验研究[J]. 钢结构, 2016, 31(10): 38-41 doi: 10.13206/j.gjg201610009

    CHI Pei, DONG Jun, XIA Zhiyuan, et al. Analysis and experimental study on the stress state of holding pole with double rotating arms for overhead transmission line construction[J]. Steel Construction, 2016, 31(10): 38-41.(in Chinese) doi: 10.13206/j.gjg201610009
    [3]
    MENG Z, REN S H, WANG X, et al. System reliability-based design optimization with interval parameters by sequential moving asymptote method[J]. Structural and Multidisciplinary Optimization, 2021, 63: 1767-1788. doi: 10.1007/s00158-020-02775-1
    [4]
    中华人民共和国国家质量监督检验检疫总局, 中国国家标准化管理委员会. 起重机设计规范: GB/T3811—2008[S]. 北京: 中国标准出版社, 2008.

    General Administration of Quality Supervision, Inspection and Quarantine of the People’s Republic of China, Standardization Administration of the People’s Republic of China. Design rules for cranes: GB/T3811—2008[S]. Beijing: Standards Press of China, 2008. (in Chinese)
    [5]
    OSMAN I H, LAPORTE G. Metaheuristics: a bibliography[J]. Annals of Operations Research, 1996, 63: 513-623.
    [6]
    BAGHDADI A, HERISTCHIAN M, KLOFT H. Design of prefabricated wall-floor building systems using metaheuristic optimization algorithms[J]. Automation in Construction, 2020, 114: 103156. doi: 10.1016/j.autcon.2020.103156
    [7]
    TAO R, MENG Z, ZHOU H L. A self-adaptive strategy based firefly algorithm for constrained engineering design problems[J]. Applied Soft Computing, 2021, 107: 107417. doi: 10.1016/j.asoc.2021.107417
    [8]
    孟建军, 孟高阳, 李德仓. 基于SA-GA混合算法的动车组车辆轮重分配优化[J]. 应用数学和力学, 2021, 42(4): 363-372

    MENG Jianjun, MENG Gaoyang, LI Decang. Optimization of weel weight distribution for EMU vehicles based on the SA-GA hybrid algroithm[J]. Applied Mathematics and Mechanics, 2021, 42(4): 363-372.(in Chinese)
    [9]
    ABUALIGAH L, DIABAT A, MIRJALILI S, et al. The arithmetic optimization algorithm[J]. Computer Methods in Applied Mechanics and Engineering, 2021, 376: 113609. doi: 10.1016/j.cma.2020.113609
    [10]
    WOLPERT D H, MACREADY W G. No free lunch theorems for optimization[J]. IEEE Transactions on Evolutionary Computation, 1997, 1(1): 67-82. doi: 10.1109/4235.585893
    [11]
    WANG R B, WANG W F, XU L, et al. An adaptive parallel arithmetic optimization algorithm for robot path planning[J]. Journal of Advanced Transportation, 2021, 2021: 3606895.
    [12]
    IZCI D, EKINCI S, KAYRI M, et al. A novel improved arithmetic optimization algorithm for optimal design of PID controlled and Bode’s ideal transfer function based automobile cruise control system[J]. Evolving Systems, 2022, 13(3): 1-16.
    [13]
    PIRES S E J, MACHADO T J A, OLIVEIRA P B D M, et al. Particle swarm optimization with fractional-order velocity[J]. Nonlinear Dynamics, 2010, 61: 295-301. doi: 10.1007/s11071-009-9649-y
    [14]
    MOUSAVI Y, ALFI A. Fractional calculus-based firefly algorithm applied to parameter estimation of chaotic systems[J]. Chaos, Solitons and Fractals, 2018, 114: 202-215. doi: 10.1016/j.chaos.2018.07.004
    [15]
    DESHMUKH A B, RANI U N. Fractional-grey wolf optimizer-based kernel weighted regression model for multiview face video super resolution[J]. International Journal of Machine Learning and Cybernetics, 2019, 10: 859-877. doi: 10.1007/s13042-017-0765-6
    [16]
    MEHMANI A, CHOWDHURY S, MEINRENKEN C, et al. Concurrent surrogate model selection (COSMOS): optimizing model type, kernel function, and hyper-parameters[J]. Structural and Multidisciplinary Optimization, 2018, 57: 1093-1114. doi: 10.1007/s00158-017-1797-y
    [17]
    FORRESTER A, SOBESTER A, KEANE A. Engineering Design via Surrogate Modelling: a Practical Guide[M]. UK: John Wiley and Sons, 2008.
    [18]
    GHAMISI P, COUCEIRO M S, BENEDIKTSSON J A, et al. An efficient method for segmentation of images based on fractional calculus and natural selection[J]. Expert Systems With Applications, 2012, 39(16): 12407-12417. doi: 10.1016/j.eswa.2012.04.078
    [19]
    MUSTOE L R, BARRY M D J. Mathematics in Science and Engineering[M]. Elsevier, 1999.
    [20]
    赵玉新, YANG Xinshe, 刘利强. 新兴元启发式优化方法[M]. 北京: 科学出版社, 2013.

    ZHAO Yuxin, YANG Xinshe, LIU Liqiang. Emerging Meta Heuristic Optimization Method[M]. Beijing: Science Press, 2013. (in Chinese)
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