Particle swarm optimization (PSO) algorithm was introduced into Kriging modeling process.By taking advantage of PSO's multi-point search ability

the limits of pattern search method's single-point search approach as well as its heavy dependence on the initial guess solution were overcome

so that the optimal correlation parameters in the maximum likelihood sense could be guaranteed under any initial conditions

and the optimal unbiased characteristic for the Kriging prediction could also be assured.A turbine disk low cycle fatigue(LCF)reliability analysis example indicates that the accuracy of the proposed PSO-Kriging in predicting the circumferential strain amplitude of the weakest point is higher than that of neural network on the order of magnitude

i.e.the maximum error decreases from 5.94% to 0.09%

so that it can replace the finite element program in Monte Carlo simulation without sacrificing the accuracy.Meanwhile

the time for PSO-Kriging's modeling and predicting is less than 1/10 consumed by a single finite element run.Due to high-accuracy prediction (the optimal unbiased characteristic assured by PSO) and relative low expense

the proposed PSO-Kriging is valuable for the reliability analysis of real engineering structures.