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| Identification of Dynamic Parameters Based on Pseudo-Parallel Ant Colony Optimization Algorithm | |
| 引用本文： | 赵凤遥 马震岳 张运良. Identification of Dynamic Parameters Based on Pseudo-Parallel Ant Colony Optimization Algorithm[J]. 西南交通大学学报(英文版), 2007, 15(2): 111-116 |
| 作者姓名： | 赵凤遥 马震岳 张运良 |
| 作者单位： | School of Civil and Hydraulic Engineering Dalian University of Technology，School of Civil and Hydraulic Engineering Dalian University of Technology，School of Civil and Hydraulic Engineering Dalian University of Technology，Dalian 116024 China School of Environment and Water Conservancy Zhengzhou University Zhengzhou 450001 China，Dalian 116024 China，Dalian 116024 China |
| 基金项目： | The National Natural Science Foundationof China (No.50279003) |
| 摘 要： | IntroductionReal ants are capable of finding the shortest pathfrom a food source to the nest. Inspired by this factand the behavior of ant colonies, a novel optimizationalgorithm called ant system (AS) was first developedby Dorigo in1992[1]. In the following years, diversemodifications of the AS algorithm were made andapplied to many different types of optimization prob-lems, and satisfactory results were obtained. Re-cently, the AS algorithm has been extended to an al-gorithm for solving d… |
| 关 键 词： | 伪平行蚁群优化算法 动态参数 参数辨识 弹性模量 |
| 文章编号： | 1005-2429（2007）02-0111-06 |
| 收稿时间： | 2006-03-03 |
Identification of Dynamic Parameters Based on Pseudo-Parallel Ant Colony Optimization Algorithm |
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| ZHAO Feng-yao,MA Zhen-yue,ZHANG Yun-liang. Identification of Dynamic Parameters Based on Pseudo-Parallel Ant Colony Optimization Algorithm[J]. Journal of Southwest Jiaotong University, 2007, 15(2): 111-116 | |
| Authors: | ZHAO Feng-yao MA Zhen-yue ZHANG Yun-liang |
| Abstract: | For the parameter identification of dynamic problems, a pseudo-parallel ant colony optimization (PPACO) algorithm based on graph-based ant system (AS) was introduced. On the platform of ANSYS dynamic analysis, the PPACO algorithm was applied to the identification of dynamic parameters successfully. Using simulated data of forces and displacements, elastic modulus E and damping ratio ξ was identified for a designed 3D finite element model, and the detailed identification step was given. Mathematical example and simulation example show that the proposed method has higher precision, faster convergence speed and stronger antinoise ability compared with the standard genetic algorithm and the ant colony optimization (ACO) algorithms. |
| Keywords: | Parameters identification Ant system Pseudo-parallel ant colony optimization (PPACO) ANSYS |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ | |