A surrogate based particle swarm optimization(SBPSO)algorithm which combines the surrogate modeling technique and particle swarm optimization is applied to the reliabilitybased robust design(RBRD)of composite pressure vessels.The algorithm and efciency of SBPSO are displayed through numerical examples.A model for filament-wound composite pressure vessels with metallic liner is then studied by netting analysis and its responses are analyzed by using Finite element method(performed by software ANSYS).An optimization problem for maximizing the performance factor is formulated by choosing the winding orientation of the helical plies in the cylindrical portion,the thickness of metal liner and the drop of region size as the design variables.Strength constraints for composite layers and the metal liner are constructed by using Tsai-Wu failure criterion and Mises failure criterion respectively.Numerical examples show that the method proposed can efectively solve the RBRD problem,and the optimal results of the proposed model can satisfy certain reliability requirement and have the robustness to the fluctuation of design variables. A surrogate based particle swarm optimization (SBPSO) algorithm which combines the surrogate modeling technique and particle swarm optimization is applied to the reliability based robust design (RBRD) of composite pressure vessels. The algorithm and efciency of SBPSO are displayed through numerical examples. A model for An optimization problem for maximizing the performance factor is formulated by choosing the winding orientation of the helical plies in the cylindrical portion, the thickness of metal liner and the drop of region size as the design variables. strength constraints for composite layers and the metal liner are constructed by using Tsai-Wu failure criterion and Mises failure criterion respectively. Numerical examples show that the method proposed can efectively solve the RBRD problem, and the optima l results of the proposed model can satisfy certain reliability requirement and have the robustness to the fluctuation of design variables.