支持向量机和粒子群优化的可靠度算法

A reliability algorithm based on support vector machine and particle swarm optimization

针对非线性隐式极限状态方程的可靠度指标计算,将支持向量机和粒子群优化算法相结合,提出了一种结构可靠度算法.首先结合支持向量机不受样本点限制的优点,将历次迭代产生样本点加入本次迭代样本点中,采用支持向量机对样本点进行训练,然后引入粒子群优化算法计算可靠度指标,解决迭代过程中支持向量机回归模型可靠度指标计算震荡不收敛的情况,最后根据可靠度指标收敛得到的支持向量机回归模型,采用重要抽样法计算失效概率.计算结果表明:该方法得出的失效概率具有较好的精度,特别是针对迭代过程中可靠度指标不收敛的情况具有良好的适用性.

To calculate the reliability index of the implicit nonlinear limit state equation, this paper proposes a structural reliability algorithm by combining the support vector machine （SVM） and particle swarm optimization （PSO）. First, each iteration sample point is added to the sample points of this iteration as training samples for SVM, based on the advantage that SVM would not be limited by sample points. Secondly, the PSO method is introduced to calculate reliability index so as to solve the situation that the reliability index calculation of nonlinear limit state equation does not converge in the iterative process. Finally, importance sampling method is adopted to calculate failure probability according to the SVM regression model from the reliability index convergence. The numerical results illustrate that the failure probability calculated by this method has better precision and especially for the non-convergence of reliability index in the iterative process.