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Identification of Dynamic Parameters Based on Pseudo-Parallel Ant Colony Optimization Algorithm

机译:基于伪并行蚁群算法的动态参数辨识

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摘要

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 anti-noise ability compared with the standard genetic algorithm and the ant colony optimization (ACO) algorithms.
机译:为了解决动态问题的参数识别问题,提出了一种基于图式蚂蚁系统的伪并行蚁群优化算法。在ANSYS动态分析平台上,将PPACO算法成功应用于动态参数识别。利用力和位移的模拟数据,为设计的3D有限元模型确定了弹性模量E和阻尼比ξ,并给出了详细的识别步骤。算例和仿真结果表明,与标准遗传算法和蚁群优化算法相比,该方法具有更高的精度,更快的收敛速度和更强的抗噪能力。

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