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Global optimization by coupled local minimizers and its application to FE model updating

机译:耦合局部最小化器的全局优化及其在有限元模型更新中的应用

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Coupled local minimizers (CLM) is a new method applicable to global optimization of functions with multiple local minima. In CLM a cooperative search mechanism is set up using a population of local optimizers, which are coupled during the search process by synchronization constraints. CLM is characterised by a relative fast convergence since the local optimizers are gradient-based. The combination of both, the coupled parallel strategy and the fast convergence, offers an efficient global optimization algorithm. In the paper the CLM method is described and is illustrated with a test function. Due to the simultaneous and coupled search of a whole population of optimizers, CLM is able to find the global minimum of the test function. Next, CLM is successfully applied to FE model updating using experimental modal data. In an example the damage pattern of a reinforced concrete beam is identified.
机译:耦合局部最小化器(CLM)是一种适用于具有多个局部最小值的函数的全局优化的新方法。在CLM中,使用大量局部优化器建立了协作搜索机制,这些局部优化器在搜索过程中通过同步约束进行耦合。由于本地优化器基于梯度,因此CLM的特点是收敛速度相对较快。耦合并行策略和快速收敛两者的结合提供了一种有效的全局优化算法。在本文中描述了CLM方法,并通过测试功能进行了说明。由于同时对整个优化程序进行搜索,因此CLM能够找到测试函数的全局最小值。接下来,使用实验模态数据将CLM成功应用于有限元模型更新。在一个示例中,确定了钢筋混凝土梁的损坏模式。

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