Regularized infill criteria for multi-objective Bayesian optimization with application to aircraft design

• URL: http://arxiv.org/abs/2504.08671v1
• Date: Fri, 11 Apr 2025 16:24:40 GMT
• Title: Regularized infill criteria for multi-objective Bayesian optimization with application to aircraft design
• Authors: Robin Grapin, Youssef Diouane, Joseph Morlier, Nathalie Bartoli, Thierry Lefebvre, Paul Saves, Jasper Bussemaker,
• Abstract summary: 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.
• Score: 0.565395466029518
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Bayesian optimization is an advanced tool to perform ecient global optimization It consists on enriching iteratively surrogate Kriging models of the objective and the constraints both supposed to be computationally expensive of the targeted optimization problem Nowadays efficient extensions of Bayesian optimization to solve expensive multiobjective problems are of high interest The proposed method in this paper extends the super efficient global optimization with mixture of experts SEGOMOE to solve constrained multiobjective problems To cope with the illposedness of the multiobjective inll criteria different enrichment procedures using regularization techniques are proposed The merit of the proposed approaches are shown on known multiobjective benchmark problems with and without constraints The proposed methods are then used to solve a biobjective application related to conceptual aircraft design with ve unknown design variables and three nonlinear inequality constraints The 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.

Related papers

• Scalable Min-Max Optimization via Primal-Dual Exact Pareto Optimization [66.51747366239299]
  We propose a smooth variant of the min-max problem based on the augmented Lagrangian.<n>The proposed algorithm scales better with the number of objectives than subgradient-based strategies.
  arXiv  Detail & Related papers  (2025-03-16T11:05:51Z)
• Constrained Multi-objective Bayesian Optimization through Optimistic Constraints Estimation [10.77641869521259]
  We propose a novel constrained multi-objective Bayesian optimization algorithm COMBOO that balances active learning of the level-set defined on multiple unknowns with multi-objective optimization within the feasible region. We provide both theoretical analysis and empirical evidence, demonstrating the efficacy of our approach on various synthetic benchmarks and real-world applications.
  arXiv  Detail & Related papers  (2024-11-06T03:38:00Z)
• 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)
• Achieving Diversity in Objective Space for Sample-efficient Search of Multiobjective Optimization Problems [4.732915763557618]
  We introduce the Likelihood of Metric Satisfaction (LMS) acquisition function, analyze its behavior and properties, and demonstrate its viability on various problems. This method presents decision makers with a robust pool of promising design decisions and helps them better understand the space of good solutions.
  arXiv  Detail & Related papers  (2023-06-23T20:42:22Z)
• Evolutionary Solution Adaption for Multi-Objective Metal Cutting Process Optimization [59.45414406974091]
  We introduce a framework for system flexibility that allows us to study the ability of an algorithm to transfer solutions from previous optimization tasks. We study the flexibility of NSGA-II, which we extend by two variants: 1) varying goals, that optimize solutions for two tasks simultaneously to obtain in-between source solutions expected to be more adaptable, and 2) active-inactive genotype, that accommodates different possibilities that can be activated or deactivated. Results show that adaption with standard NSGA-II greatly reduces the number of evaluations required for optimization to a target goal, while the proposed variants further improve the adaption costs.
  arXiv  Detail & Related papers  (2023-05-31T12:07:50Z)
• ARES: An Efficient Algorithm with Recurrent Evaluation and Sampling-Driven Inference for Maximum Independent Set [48.57120672468062]
  This paper introduces an efficient algorithm for the Maximum Independent Set (MIS) problem, incorporating two innovative techniques. The proposed algorithm outperforms state-of-the-art algorithms in terms of solution quality, computational efficiency, and stability.
  arXiv  Detail & Related papers  (2022-08-16T14:39:38Z)
• 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)
• Algorithm for Constrained Markov Decision Process with Linear Convergence [55.41644538483948]
  An agent aims to maximize the expected accumulated discounted reward subject to multiple constraints on its costs. A new dual approach is proposed with the integration of two ingredients: entropy regularized policy and Vaidya's dual. The proposed approach is shown to converge (with linear rate) to the global optimum.
  arXiv  Detail & Related papers  (2022-06-03T16:26:38Z)
• GPSAF: A Generalized Probabilistic Surrogate-Assisted Framework for Constrained Single- and Multi-objective Optimization [7.8140593450932965]
  This paper proposes a generalized probabilistic surrogate-assisted framework (GPSAF) GPSAF is applicable to a broad category of unconstrained and constrained, single- and multi-objective optimization algorithms.
  arXiv  Detail & Related papers  (2022-04-06T13:22:30Z)
• Multi-objective robust optimization using adaptive surrogate models for problems with mixed continuous-categorical parameters [0.0]
  Robust design optimization is traditionally considered when uncertainties are mainly affecting the objective function. The resulting nested optimization problem may be solved using a general-purpose solver, herein the non-dominated sorting genetic algorithm (NSGA-II) The proposed approach consists of sequentially carrying out NSGA-II while using an adaptively built Kriging model to estimate the quantiles.
  arXiv  Detail & Related papers  (2022-03-03T20:23:18Z)
• Faster Algorithm and Sharper Analysis for Constrained Markov Decision Process [56.55075925645864]
  The problem of constrained decision process (CMDP) is investigated, where an agent aims to maximize the expected accumulated discounted reward subject to multiple constraints. A new utilities-dual convex approach is proposed with novel integration of three ingredients: regularized policy, dual regularizer, and Nesterov's gradient descent dual. This is the first demonstration that nonconcave CMDP problems can attain the lower bound of $mathcal O (1/epsilon)$ for all complexity optimization subject to convex constraints.
  arXiv  Detail & Related papers  (2021-10-20T02:57:21Z)
• Momentum Accelerates the Convergence of Stochastic AUPRC Maximization [80.8226518642952]
  We study optimization of areas under precision-recall curves (AUPRC), which is widely used for imbalanced tasks. We develop novel momentum methods with a better iteration of $O (1/epsilon4)$ for finding an $epsilon$stationary solution. We also design a novel family of adaptive methods with the same complexity of $O (1/epsilon4)$, which enjoy faster convergence in practice.
  arXiv  Detail & Related papers  (2021-07-02T16:21:52Z)

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.