Parameter estimation using a CLPSO strategy
Parameter estimation using a CLPSO strategy
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DOI：
10.1109/cec.2008.4630778
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发表时间：
2008-06
期刊：
影响因子：
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通讯作者：
He-Sheng Tang;W. Zhang;C. Fan;Song-Tao Xue
中科院分区：
文献类型：
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作者：
He-Sheng Tang;W. Zhang;C. Fan;Song-Tao Xue
粒子群优化算法（PSO）作为一种新型的进化计算技术，在解决不同领域的复杂优化问题，尤其是各种连续优化问题方面，得到了广泛的应用。然而，它可能很容易陷入局部最优解时，解决复杂的多峰问题。本文利用一种改进的粒子群优化算法（CLPSO）对结构系统进行参数估计，将结构系统的参数估计问题转化为高维多模态优化问题。在输出数据有限且没有质量、阻尼、刚度等先验知识的条件下，对结构系统参数进行了辨识，仿真结果验证了该方法的有效性。
As a novel evolutionary computation technique, particle swarm optimization (PSO) has attracted much attention and wide applications for solving complex optimization problems in different fields mainly for various continuous optimization problems. However, it may easily get trapped in a local optimum when solving complex multimodal problems. This paper utilizes an improved PSO by incorporating a comprehensive learning strategy into original PSO to discourage premature convergence, namely CLPSO strategy to estimate parameters of structural systems, which could be formulated as a multi-modal optimization problem with high dimension. Simulation results for identifying the parameters of a structural system under conditions including limited output data and no prior knowledge of mass, damping, or stiffness are presented to demonstrate the effectiveness of the proposed method.