基于等级划分狼群算法的三维传感器优化布置方法研究

Hierarchic wolf algorithm for optimal triaxial sensor placement

为解决传感器优化布置中的信息冗余问题,提出了一种信息冗余度函数,将其与三维模态置信准则(TMAC)相结合,建立了一种既能保证模态振型可观性又能保证模态振型可区分性的传感器三维模态置信准则。为提高算法的求解效率,提出了一种等级划分狼群算法,采用双重编码的方式,克服了原狼群算法只能求解连续变量优化的问题;通过人工均匀法进行狼群数据的初始化,以保证初始数据的均匀性;并采用等级划分方法,避免群体内狼个体与头狼等级相似,增加狼群的多样性,提高算法的搜索效率。以一个桥梁基准模型为数值算例,进行参数敏感性分析以及三维传感器优化布置方案的选择。结果表明:等级划分狼群算法的搜索能力较原狼群算法有了大幅提高,能较好地解决传感器优化布置问题。

In order to solve the information redundancy problem in the optimal sensor placement （0SP）, the information redundancy function fl（R） was proposed, and then the triaxial modal assurance criteria considering the redundancy of information was established by combining g（R） with the triaxial modal assurance criterion （TMAC）, which can ensure both the observability and discriminability of the modal shape. Furthermore, to improve the solution efficiency, a novel hierarchic wolf algorithm （HWA） was put forward. First, the dual-structure coding method was used to overcome that the original wolf algorithm （VIA） can only solve the optimization of continuous variables, and the artificial uniform distribution method was raised for the initialization of the wolf population to ensure the uniformity of the initial data. Then, the hierarchic method was adopted to avoid that any individual wolf has the similar grade with the wolf king, which may enhance the diversity of the wolf population and improve the searching efficiency of the algorithm. Finally, the parametric sensitivity analysis and the OSP selection were performed on the benchmark of Highway Bridge. The results indicate that the searching ability of the HWA greatly increases compared with the original WA, which can better solve the OSP problem.