Non‐parametric measure approximations for constrained multi‐objective optimisation under uncertainty

In this article, we propose non‐parametric estimations of robustness and reliability measures approximation error, employed in the context of constrained multi‐objective optimisation under uncertainty (OUU). These approximations with tunable accuracy permit to capture the Pareto front in a parsimoni...

Full description

Saved in:
Bibliographic Details
Published inInternational journal for numerical methods in engineering Vol. 125; no. 7
Main Authors Rivier, M., Razaaly, N., Congedo, P.M.
Format Journal Article
LanguageEnglish
Published Hoboken, USA John Wiley & Sons, Inc 15.04.2024
Wiley Subscription Services, Inc
Wiley
Subjects
Accuracy
Approximation
Computation
Constraint modelling
Engineering Sciences
Error analysis
Gaussian processes
optimisation under uncertainty
Optimization
organic Rankine cycle
Pareto optimum
Rankine cycle
Reliability
Robustness
robustness and reliability
Turbines
Uncertainty
Robustness and reliability
Optimisation under uncertainty
Organic Rankine cycle
Gaussian processes
Online AccessGet full text

Cover

More Information
Summary:In this article, we propose non‐parametric estimations of robustness and reliability measures approximation error, employed in the context of constrained multi‐objective optimisation under uncertainty (OUU). These approximations with tunable accuracy permit to capture the Pareto front in a parsimonious way, and can be exploited within an adaptive refinement strategy. First, we illustrate an efficient approach for obtaining joint representations of the robustness and reliability measures, allowing sharper discrimination of Pareto‐optimal designs. A specific surrogate model of these objectives and constraints is then proposed to accelerate the optimisation process. Secondly, we propose an adaptive refinement strategy, using these tunable accuracy approximations to drive the computational effort towards the computation of the optimal area. To this extent, an adapted Pareto dominance rule and Pareto optimal probability computation are formulated. The performance of the proposed strategy is assessed on several analytical test‐cases against classical approaches. We also illustrate the method on an engineering application, performing shape OUU of an Organic Rankine Cycle turbine.
ISSN:0029-5981
1097-0207
DOI:10.1002/nme.7403