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Model updating using genetic algorithms with sequential niche technique

机译:使用遗传算法和顺序小生境技术进行模型更新

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Structural model updating is an optimisation problem where parameters that minimise the errors between the model and the actual structure are sought. However, multiple solutions may be present. Global optimisation algorithms are efficient optimisation tools but are not fully immune to missing the global minimum. To increase the chance of finding the global minimum, a combination of genetic algorithm with sequential niche technique is proposed. The method performs systematic search to find multiple minima and facilitates detecting the minimum that best describes the system. The technique is applied to experimental data from a simple laboratory structure and a full-scale pedestrian cable-stayed bridge, and also tested on a deceptive problem using the numerical model of a space frame. (C) 2016 Elsevier Ltd. All rights reserved.
机译:结构模型更新是一个优化问题,其中寻求使模型与实际结构之间的误差最小的参数。但是,可能存在多种解决方案。全局优化算法是有效的优化工具,但不能完全避免丢失全局最小值。为了增加找到全局最小值的机会,提出了一种将遗传算法与顺序小生境技术相结合的方法。该方法执行系统搜索以找到多个最小值,并有助于检测最能描述系统的最小值。该技术被应用于来自简单实验室结构和全尺寸人行斜拉桥的实验数据,并且还使用空间框架的数值模型对欺骗性问题进行了测试。 (C)2016 Elsevier Ltd.保留所有权利。

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