A novel hybrid PSO–SVR method is proposed that combines support vector regression (SVR) with the particle swarm optimization (PSO) algorithm to improve the computational efficiency of predicting structural failure. To express the influence of each parameter on the results of the approximate model, weights are introduced into the kernel function, and the method is called weighted SVR (WSVR). To select the optimal SVR parameters to improve the fitting results of the WSVR response surface model, the PSO algorithm is used. To improve the PSO's convergence rate and its ability of jump out of local extrema, a parasitic immune particle swarm optimization (PIPSO) algorithm is proposed. The basic idea of PIPSO is that an exploration strategy and a high frequency of immune system mutations are used for the particles of the host population to expand the search space of the algorithm and suppress premature convergence. Results from computational tests show that PIPSO has a faster convergence speed and a better search accuracy than PSO, linearly decreasing inertia weight PSO (LDIWPSO), and e₂-PSO. More importantly, the PIPSO–WSVR method has a higher accuracy and efficiency than the SPSO–SVR, LDIWPSO–SVR, and e₂-PSO–SVR methods with the same sample size. Therefore, the proposed PIPSO-WSVR shows promising results for practical structural reliability analysis.

提出了一种新颖的混合PSO-SVR方法，该方法将支持向量回归（SVR）与粒子群优化（PSO）算法相结合，以提高预测结构破坏的计算效率。为了表达每个参数对近似模型结果的影响，将权重引入内核函数，该方法称为加权SVR（WSVR）。为了选择最佳SVR参数以改善WSVR响应面模型的拟合结果，使用了PSO算法。为了提高粒子群算法的收敛速度和跳出局部极值的能力，提出了一种寄生免疫粒子群算法（PIPSO）。PIPSO的基本思想是将探索策略和免疫系统突变的频率用于宿主种群的粒子，以扩展算法的搜索空间并抑制过早收敛。计算测试的结果表明，与PSO相比，PIPSO的收敛速度更快，搜索精度更高，惯性权重PSO（LDIWPSO）线性减小，并且e ₂ -PSO。更重要的是，与相同样本量的SPSO-SVR，LDIWPSO-SVR和e ₂ -PSO-SVR方法相比，PIPSO-WSVR方法具有更高的准确性和效率。因此，所提出的PIPSO-WSVR为实用的结构可靠性分析显示出令人鼓舞的结果。