The comparison of robust and reliability based design optimization

Abstract

This article compares two optimization methods considering random variations in design parameters. One is reliability-based design optimization, which depends on the availability of the joint probability density function. A more practical alternative is robust optimization, which does not require the estimation of failure probability. It accounts for the random response of the structure through definitions of objective functions and constraints, incorporating mean values and response variances. An important element of the algorithm involves approximating unknown responses of the structures and employing efficient statistical moment estimation methods. The kriging method was used in this paper. Additionally, the article evaluates two experimental plan techniques: the classical random sampling plan and the OLH plan.