为了使有限元模型修正结果更加符合结构实际情况，将传统的提供单一修正结果转变为提供多个修正结果，然后由决策者根据现场情况、类似工程经验等非参数信息来决定最终采用的修正模型；并且针对这一问题，将优化速度快的稳态遗传算法和优化精度高的梯度下降算法相结合，提出了一种混合智能算法.最后分别采用数值算例和ASCE-Benchmark模型修正过程验证了所提算法的寻找多解能力和优化精度.结果表明，本文所提算法可以寻找到定义域内的全部极值，且相比于稳态遗传算法具有更高的精度，ASCE-Benchmark算例中，两个修正后的有限元模型与实测结果之间的频率误差均有明显下降.
To make the result of finite element model updating more accord with the real structure, this paper converts to provide multiple alternatives, instead of offering just a single result. With those options, the policy makers can apply their working experience and consider field condition, which leads to more suitable decision. This paper proposes a hyper algorithm which combines the steady-state genetic algorithm and the gradient descent algorithm. A numerical simulation and an ASCE-Benchmark problem are employed to verify the ability to find multiple alternatives and optimization accuracy of the proposed method. The results show that the algorithm can optimize all the minimums and show a better accuracy compared to the steady-state genetic algorithm. After updating the frequency, error between the finite element model and real structure is significantly reduced.
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