改进布谷鸟算法在结构可靠性分析中的应用

在计算过程中，标准布谷鸟算法（cuckoo search algorithm，CS）中的参数是保持不变的，这影响了该算法的收敛性和计算精度。为了克服这一缺陷，首先探讨了标准CS中飞行步长和淘汰概率两个关键参数的变化规律对该算法全局搜索与局部搜索能力的影响，然后对这两个参数进行了自适应改进，同时，提出了一个具有全局最优导向的搜索方程以进一步提高CS的局部搜索能力和收敛速度。利用改进后的CS与人工神经网络响应面法相结合进行结构可靠性分析。算例分析说明，与标准CS以及粒子群算法和遗传算法相比，所提出的改进CS在进行结构可靠性分析中，能够有效地减少计算时间并提高解的精度。

Improved cuckoo search algorithm for structural reliability analysis

In the iterations, the parameters of standard cuckoo search algorithm (CS) are constant, which may affect the convergence and accuracy of the algorithm. To overcome this defection, the variations of the two main parameters which affect the global search and local search capabilities are investigated, and then improvements are made to the parameters. In addition, a modified search equation which aims to further improve the CS local search ability and convergence speed is proposed. The improved CS combined with artificial neural network respond surface method is proposed to solve the structural reliability problem. Comparison with the standard CS, particle swarm algorithm and genetic algorithm, the proposed improved CS reduces the computation and improves the accuracy of the solutions effectively in the process of structural reliability analysis.