中山大学学报自然科学版 ›› 2017, Vol. 56 ›› Issue (2): 62-65.

• 研究论文 • 上一篇    下一篇

基于改进萤火虫算法的结构损伤识别

胡磊, 吕中荣, 刘济科   

  1. 中山大学工学院,广东 广州 510006
  • 收稿日期:2016-07-15 出版日期:2017-03-25 发布日期:2017-03-25

Damage identification based on improved GSO algorithm

HU Lei , Lv Zhongrong, LIU Jike   

  1. School of Engineering, Sun Yat-sen University,Guangzhou 510006,China
  • Received:2016-07-15 Online:2017-03-25 Published:2017-03-25

摘要:

提出一种基于改进萤火虫算法的结构损伤识别方法。将结构损伤模拟成杨氏模量的减少。对于没有邻居的萤火虫,让它们在自身位置附近随机搜索,同时引入新的移动方式来提高算法的精度和收敛速度,避免算法过早的陷入局部最优,克服萤火虫算法在高维目标函数中寻优能力不足的问题。采取功能梯度梁(Axial functionally graded, AFG)作为研究对象,利用欧拉-伯努利梁单元建立力学模型。采用了简支梁作为算例,将结果和基本萤火虫算法、领导者萤火虫和自适应步长萤火虫算法作对比,说明改进的有效性。

关键词: 损伤识别, 萤火虫算法, 功能梯度梁, 高维目标函数

Abstract:

An approach based on improved glowworm swarm optimization (GSO) for structure damage detection is presented. The local damage is simulated by a reduction in the elemental Youngs modulus of the beam .In order to enhance accuracy and convergence rate, the perturbation of the glowworm without neighbor is offered and a new search strategy is introduced in the movement phase of GSO to avoid local optima and enhance the GSO algorithm in high dimensions space target function optimization question solution ability. On the other hand, Axial functionally graded (AFG) beam with the assumptions of Euler-Bernoulli beam theory is adopted to establish the dynamic equation. The numerical experiments with a simply supported beam is carried to illustrate the efficiency of the proposed improvement GSO. The result reveals that the proposed method is more accurate compared to the original GSO, leader GSO (LGSO),and the variation step adaptive GSO.

Key words: damage identification, glowworm swarm optimization, axial functionally graded (AFG) beam, high dimensional objective function

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