Abstract: An evidence theory and interval analysis-based method of reliability design optimization is proposed. For the preconcerted failure probability f0, the plausibility measure of reliability constraint, Pl(F), is calculated by using evidence theory. With Pl(F)f0 used as a surrogate model of reliability constraint, an evidence theory-based model of generalized reliability design optimization is formulated. If there is only random uncertainty in the formulated model, this model degenerates to a conventional model of reliability design optimization. A numerical method for calculating Pl(F) is presented. The theory of interval analysis is used for approximately calculating the value range of constraint function, then the computational efficiency of Pl(F) is significantly improved and the computational cost for solving the reliability optimization model is reduced. A genetic algorithm program for the evidence theory and interval analysis-based reliability design optimization is developed. The proposed method is demonstrated with a pressure vessel example. The example shows that the proposed method is effective and practical.

Key words: Design optimization, Evidence theory, Interval analysis, Reliability