Local Latin Hypercube Refinement for Multi-objective Design Uncertainty Optimization

TL;DR

This paper introduces a local Latin hypercube refinement strategy for surrogate-based multi-objective robust design optimization, improving sample efficiency and results across various models and benchmarks.

Contribution

The proposed LoLHR method is a model-agnostic, sequential sampling strategy that enhances surrogate model accuracy in high-dimensional, multi-modal systems.

Findings

LoLHR outperforms stationary sampling in tested examples.

The method improves surrogate model accuracy and optimization results.

It is compatible with Gaussian process and support vector regression models.

Abstract

Optimizing the reliability and the robustness of a design is important but often unaffordable due to high sample requirements. Surrogate models based on statistical and machine learning methods are used to increase the sample efficiency. However, for higher dimensional or multi-modal systems, surrogate models may also require a large amount of samples to achieve good results. We propose a sequential sampling strategy for the surrogate based solution of multi-objective reliability based robust design optimization problems. Proposed local Latin hypercube refinement (LoLHR) strategy is model-agnostic and can be combined with any surrogate model because there is no free lunch but possibly a budget one. The proposed method is compared to stationary sampling as well as other proposed strategies from the literature. Gaussian process and support vector regression are both used as surrogate models. Empirical evidence is presented, showing that LoLHR achieves on average better results compared to other surrogate based strategies on the tested examples.