A Particle Swarm Optimization Algorithm with Sigmoid Increasing Inertia Weight for Structural Damage Identification

被引：4
|
作者：
Chen, Zhen [1 ,2 ]
Wang, Yaru [1 ]
Chan, Tommy H. T. [2 ]
Li, Xiaoke [1 ]
Zhao, Shunbo [1 ,3 ]
机构：
[1] North China Univ Water Resources & Elect Power, Sch Civil Engn & Commun, Zhengzhou 450045, Peoples R China
[2] Queensland Univ Technol QUT, Sch Civil & Environm Engn, Brisbane, Qld 4000, Australia
[3] North China Univ Water Resources & Elect Power, Collaborat Innovat Ctr Efficient Utilizat Water R, Zhengzhou 450046, Peoples R China
来源：
APPLIED SCIENCES-BASEL | 2022年 / 12卷 / 07期
基金：
中国国家自然科学基金;
关键词：
structural damage identification; particle swarm optimization; inertia weight; identification accuracy; ADAPTATION;
D O I：
10.3390/app12073429
中图分类号：
O6 [化学];
学科分类号：
0703 ;
摘要：
In this study, a particle swarm optimization with a sigmoid increasing inertia weight (SIPSO) algorithm is proposed for structural damage identification based on the optimization of structural vibration response constraints. In view of the existing problems for particle swarm optimization algorithms used for structural damage identification, such as low accuracy of damage identification and easy misjudgment of damage location, the sigmoid increasing inertia weight is introduced to improve the global and local search ability of the algorithm. Simulation results show that the parameters of the sigmoid increasing inertia weight have a significant effect on the performance of the SIPSO algorithm for structural damage identification. Compared with similar improved particle swarm optimization algorithms, the SIPSO algorithm has some advantages of fast convergence speed, high identification accuracy, and strong robustness ability in structural damage identification.
引用
收藏
页数：20
相关论文
共 50 条
  • [1] THE INFLUENCE OF INERTIA WEIGHT ON THE PARTICLE SWARM OPTIMIZATION ALGORITHM
    Cekus, Dawid
    Skrobek, Dorian
    JOURNAL OF APPLIED MATHEMATICS AND COMPUTATIONAL MECHANICS, 2018, 17 (04) : 5 - 11
  • [2] Inertia Weight Adaption in Particle Swarm Optimization Algorithm
    Zhou, Zheng
    Shi, Yuhui
    ADVANCES IN SWARM INTELLIGENCE, PT I, 2011, 6728 : 71 - 79
  • [3] A dynamic inertia weight particle swarm optimization algorithm
    Jiao, Bin
    Lian, Zhigang
    Gu, Xingsheng
    CHAOS SOLITONS & FRACTALS, 2008, 37 (03) : 698 - 705
  • [4] A novel particle swarm optimization algorithm with adaptive inertia weight
    Nickabadi, Ahmad
    Ebadzadeh, Mohammad Mehdi
    Safabakhsh, Reza
    APPLIED SOFT COMPUTING, 2011, 11 (04) : 3658 - 3670
  • [5] A Novel Flexible Inertia Weight Particle Swarm Optimization Algorithm
    Amoshahy, Mohammad Javad
    Shamsi, Mousa
    Sedaaghi, Mohammad Hossein
    PLOS ONE, 2016, 11 (08):
  • [6] Hybrid optimization of emission and economic dispatch by the sigmoid decreasing inertia weight particle swarm optimization
    Pitono, Joko
    Soeprijanto, Adi
    Hiyama, Takashi
    World Academy of Science, Engineering and Technology, 2009, 36 : 315 - 320
  • [7] A hybrid Particle Swarm Optimization - Simplex algorithm (PSOS) for structural damage identification
    Begambre, O.
    Laier, J. E.
    ADVANCES IN ENGINEERING SOFTWARE, 2009, 40 (09) : 883 - 891
  • [8] Adaptive particle swarm optimization algorithm with dynamically changing inertia weight
    Zhang, Ding-Xue
    Guan, Zhi-Hong
    Liu, Xin-Zhi
    Kongzhi yu Juece/Control and Decision, 2008, 23 (11): : 1253 - 1257
  • [9] A resilient particle swarm optimization algorithm with dynamically changing inertia weight
    Dong, Wu Zhi
    Hua, Zhou Sui
    Min, Feng Shi
    Jing, Xiao Zu
    ADVANCES IN MANUFACTURING SCIENCE AND ENGINEERING, PTS 1-4, 2013, 712-715 : 2423 - 2427
  • [10] Chaotic Particle Swarm Optimization Algorithm Based on Adaptive Inertia Weight
    Li, Jun-wei
    Cheng, Yong-mei
    Chen, Ke-zhe
    26TH CHINESE CONTROL AND DECISION CONFERENCE (2014 CCDC), 2014, : 1310 - 1315
12345