中国机械工程 ›› 2010, Vol. 21 ›› Issue (21): 2627-2631.

• 材料工程 • 上一篇    下一篇

基于遗传蚁群融合算法的超弹性材料参数识别

陈少伟;成艾国;胡朝辉;何智成
  

  1. 湖南大学汽车车身先进设计制造国家重点实验室,长沙,410082
  • 出版日期:2010-11-10 发布日期:2010-11-12
  • 基金资助:
    长江学者和创新团队发展计划资助项目(531105050037);湖南大学汽车车身先进设计制造国家重点实验室自主研究课题资助项目(60870002)
    Supported by Program for Changjiang Scholars and Innovative Research Team in University(No. 531105050037)

Parameter Identification of Hyperelastic Constitutive Model Based on Hybrid Algorithm Combining Ant Colony Algorithm and Genetic Algorithm

Chen Shaowei;Cheng Aiguo;Hu Zhaohui;He Zhicheng
  

  1. State Key Laboratory of Advanced Design and Manufacture for Vehicle Body,Hunan University, Changsha ,410082
  • Online:2010-11-10 Published:2010-11-12
  • Supported by:
    Supported by Program for Changjiang Scholars and Innovative Research Team in University(No. 531105050037)

摘要:

引入一种新的基于遗传蚁群融合算法的优化策略,构造超弹性材料的本构模型来分析反向问题,同时采用最小二乘法使仿真计算值和目标值之间的差值最小。该算法融合蚁群算法(ACA)的特点与遗传算法(GA)的交叉、变异进化策略,改善了解空间搜索的全局性。将该算法与GA进行了比较,结果显示该算法具有较好的优化精度。算例的最终结果也显示该算法在实际工程应用中具备一定的实用性。

关键词:

Abstract:

A new hybrid algorithm was introduced constructing the hyperelastic material constitutive model based on combination of genetic algorithm and ant colony algorithm optimization strategy to analyze the reverse problems. The least-square method was adopted to minimum the difference between the simulation values and target values. The new algorithm employs ACA features, GA crossover and mutation evolutionary strategy to improve features of the global search. At the same time the algorithm was compared with GA, showing a better accuracy. The final results of the example herein also show that the algorithm has certain practicality in real engineering applications.

Key words: hyperelastic, genetic algorithm(GA);ant colony algorithm(ACA);combination, inverse analysis, parameter identification

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