Design optimization of stochastic complex systems via iterative density estimation

TL;DR

This paper introduces an efficient iterative density estimation method for reliability-based design optimization that reduces computational costs by adaptively partitioning the design space based on failure sample distributions.

Contribution

The paper presents a novel iterative density estimation approach that improves efficiency in RBDO by adaptively partitioning the design space for failure probability approximation.

Findings

Significant reduction in computational time demonstrated.

Effective partitioning improves failure probability estimation accuracy.

Method outperforms traditional density estimation in complex systems.

Abstract

Reliability-based design optimization (RBDO) provides a rational and sound framework for finding the optimal design while taking uncertainties into ac-count. The main issue in implementing RBDO methods, particularly stochastic simu-lation based ones, is the computational burden arising from the evaluation of reliability constraints. In this contribution, we propose an efficient method which ap-proximates the failure probability functions (FPF) to decouple reliability. Based on the augmentation concept, the approximation of FPF is equivalent to density estimation of failure design samples. Unlike traditional density estimation schemes, where the esti-mation is conducted in the entire design space, in the proposed method we iteratively partition the design space into several subspaces according to the distribution of fail-ure design samples. Numerical results of an illustrative example indicate that the pro-posed method can improve the computational performance considerably.