A Derivative-Free Trust-Region Algorithm for Reliability-Based Optimization

TL;DR

This paper introduces a derivative-free trust-region algorithm for reliability-based optimization that uses surrogate models and a sample reweighting method to efficiently evaluate failure probabilities, demonstrating competitive performance.

Contribution

The paper proposes a novel derivative-free trust-region method with a sample reweighting approach for reliability constraints, reducing the number of reliability evaluations needed.

Findings

Algorithm is competitive with existing methods.

Uses only one full reliability evaluation per iteration.

Employs surrogate models for reliability constraints.

Abstract

In this note, we present a derivative-free trust-region (TR) algorithm for reliability based optimization (RBO) problems. The proposed algorithm consists of solving a set of subproblems, in which simple surrogate models of the reliability constraints are constructed and used in solving the subproblems. Taking advantage of the special structure of the RBO problems, we employ a sample reweighting method to evaluate the failure probabilities, which constructs the surrogate for the reliability constraints by performing only a single full reliability evaluation in each iteration. With numerical experiments, we illustrate that the proposed algorithm is competitive against existing methods.