Multi-fidelity approaches for general constrained Bayesian optimization with application to aircraft design

• URL: http://arxiv.org/abs/2603.28987v1
• Date: Mon, 30 Mar 2026 20:36:13 GMT
• Title: Multi-fidelity approaches for general constrained Bayesian optimization with application to aircraft design
• Abstract summary: We show how incorporating information from both the objective and the constraints can further reduce computational costs without compromising the optimality of the solution.<n>We show that employing our proposed multi-fidelity approach leads to $86%$ to $200%$ more constraint compliant solutions given a limited budget.
• Score: 0.34998703934432673
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Aircraft design relies heavily on solving challenging and computationally expensive Multidisciplinary Design Optimization problems. In this context, there has been growing interest in multi-fidelity models for Bayesian optimization to improve the MDO process by balancing computational cost and accuracy through the combination of high- and low-fidelity simulation models, enabling efficient exploration of the design process at a minimal computational effort. In the existing literature, fidelity selection focuses only on the objective function to decide how to integrate multiple fidelity levels, balancing precision and computational cost using variance reduction criteria. In this work, we propose novel multi-fidelity selection strategies. Specifically, we demonstrate how incorporating information from both the objective and the constraints can further reduce computational costs without compromising the optimality of the solution. We validate the proposed multi-fidelity optimization strategy by applying it to four analytical test cases, showcasing its effectiveness. The proposed method is used to efficiently solve a challenging aircraft wing aero-structural design problem. The proposed setting uses a linear vortex lattice method and a finite element method for the aerodynamic and structural analysis respectively. We show that employing our proposed multi-fidelity approach leads to $86\%$ to $200\%$ more constraint compliant solutions given a limited budget compared to the state-of-the-art approach.

Related papers

• A Framework for Combining Optimization-Based and Analytic Inverse Kinematics [53.66628333687382]
  Twolytic and optimization methods for inverse kinematics (IK) problems have been deeply studied in the history of robotics.<n>We present a new formulation for IK avoidance that uses an IK solution that is fundamentally easier for ours to solve.
  arXiv  Detail & Related papers  (2026-02-04T22:23:32Z)
• TCPO: Thought-Centric Preference Optimization for Effective Embodied Decision-making [75.29820290660065]
  This paper proposes Thought-Centric Preference Optimization ( TCPO) for effective embodied decision-making.<n>It emphasizes the alignment of the model's intermediate reasoning process, mitigating the problem of model degradation.<n>Experiments in the ALFWorld environment demonstrate an average success rate of 26.67%, achieving a 6% improvement over RL4VLM.
  arXiv  Detail & Related papers  (2025-09-10T11:16:21Z)
• Preference Optimization for Combinatorial Optimization Problems [54.87466279363487]
  Reinforcement Learning (RL) has emerged as a powerful tool for neural optimization, enabling models learns that solve complex problems without requiring expert knowledge.<n>Despite significant progress, existing RL approaches face challenges such as diminishing reward signals and inefficient exploration in vast action spaces.<n>We propose Preference Optimization, a novel method that transforms quantitative reward signals into qualitative preference signals via statistical comparison modeling.
  arXiv  Detail & Related papers  (2025-05-13T16:47:00Z)
• Regularized infill criteria for multi-objective Bayesian optimization with application to aircraft design [0.565395466029518]
  The proposed method in this paper extends the super efficient global optimization with mixture of experts SEGOMOE to solve constrained multiobjective problems.<n>The merit of the proposed approaches are shown on known multiobjective benchmark problems with and without constraints.<n>Preliminary results show a reduction of the total cost in terms of function evaluations by a factor of 20 compared to the evolutionary algorithm NSGA-II.
  arXiv  Detail & Related papers  (2025-04-11T16:24:40Z)
• End-to-End Learning for Fair Multiobjective Optimization Under Uncertainty [55.04219793298687]
  The Predict-Then-Forecast (PtO) paradigm in machine learning aims to maximize downstream decision quality. This paper extends the PtO methodology to optimization problems with nondifferentiable Ordered Weighted Averaging (OWA) objectives. It shows how optimization of OWA functions can be effectively integrated with parametric prediction for fair and robust optimization under uncertainty.
  arXiv  Detail & Related papers  (2024-02-12T16:33:35Z)
• Bayesian Quality-Diversity approaches for constrained optimization problems with mixed continuous, discrete and categorical variables [0.3626013617212667]
  A new Quality-Diversity methodology based on mixed variables is proposed in the context of limited simulation budget. The proposed approach provides valuable trade-offs for decision-markers for complex system design.
  arXiv  Detail & Related papers  (2023-09-11T14:29:47Z)
• Backpropagation of Unrolled Solvers with Folded Optimization [55.04219793298687]
  The integration of constrained optimization models as components in deep networks has led to promising advances on many specialized learning tasks. One typical strategy is algorithm unrolling, which relies on automatic differentiation through the operations of an iterative solver. This paper provides theoretical insights into the backward pass of unrolled optimization, leading to a system for generating efficiently solvable analytical models of backpropagation.
  arXiv  Detail & Related papers  (2023-01-28T01:50:42Z)
• Optimal Design of Electric Machine with Efficient Handling of Constraints and Surrogate Assistance [5.387300498478744]
  This article proposes an optimization method incorporated into a popularly-used evolutionary multi-objective optimization algorithm - NSGA-II. The proposed method exploits the inexpensiveness of geometric constraints to generate feasible designs by using a custom repair operator.
  arXiv  Detail & Related papers  (2022-06-03T17:13:29Z)
• Multi-Fidelity Multi-Objective Bayesian Optimization: An Output Space Entropy Search Approach [44.25245545568633]
  We study the novel problem of blackbox optimization of multiple objectives via multi-fidelity function evaluations. Our experiments on several synthetic and real-world benchmark problems show that MF-OSEMO, with both approximations, significantly improves over the state-of-the-art single-fidelity algorithms.
  arXiv  Detail & Related papers  (2020-11-02T06:59:04Z)
• Uncertainty aware Search Framework for Multi-Objective Bayesian Optimization with Constraints [44.25245545568633]
  We consider the problem of constrained multi-objective (MO) blackbox optimization using expensive function evaluations. We propose a novel framework named Uncertainty-aware Search framework for Multi-Objective Optimization with Constraints. We show that USeMOC is able to achieve more than 90 % reduction in the number of simulations needed to uncover optimized circuits.
  arXiv  Detail & Related papers  (2020-08-16T23:34:09Z)
• Optimal Bayesian experimental design for subsurface flow problems [77.34726150561087]
  We propose a novel approach for development of chaos expansion (PCE) surrogate model for the design utility function. This novel technique enables the derivation of a reasonable quality response surface for the targeted objective function with a computational budget comparable to several single-point evaluations.
  arXiv  Detail & Related papers  (2020-08-10T09:42:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.