Constrained Multi-objective Bayesian Optimization through Optimistic Constraints Estimation

• URL: http://arxiv.org/abs/2411.03641v1
• Date: Wed, 06 Nov 2024 03:38:00 GMT
• Title: Constrained Multi-objective Bayesian Optimization through Optimistic Constraints Estimation
• Authors: Diantong Li, Fengxue Zhang, Chong Liu, Yuxin Chen,
• Abstract summary: CMOBO balances learning of the feasible region with multi-objective optimization within the feasible region in a principled manner. We provide both theoretical justification and empirical evidence, demonstrating the efficacy of our approach on various synthetic benchmarks and real-world applications.
• Score: 10.77641869521259
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multi-objective Bayesian optimization has been widely adopted in scientific experiment design, including drug discovery and hyperparameter optimization. In practice, regulatory or safety concerns often impose additional thresholds on certain attributes of the experimental outcomes. Previous work has primarily focused on constrained single-objective optimization tasks or active search under constraints. We propose CMOBO, a sample-efficient constrained multi-objective Bayesian optimization algorithm that balances learning of the feasible region (defined on multiple unknowns) with multi-objective optimization within the feasible region in a principled manner. We provide both theoretical justification and empirical evidence, demonstrating the efficacy of our approach on various synthetic benchmarks and real-world applications.

Related papers

• 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. The merit of the proposed approaches are shown on known multiobjective benchmark problems with and without constraints. 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)
• Preference-Guided Diffusion for Multi-Objective Offline Optimization [64.08326521234228]
  We propose a preference-guided diffusion model for offline multi-objective optimization. Our guidance is a preference model trained to predict the probability that one design dominates another. Our results highlight the effectiveness of classifier-guided diffusion models in generating diverse and high-quality solutions.
  arXiv  Detail & Related papers  (2025-03-21T16:49:38Z)
• 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. The proposed algorithm scales better with the number of objectives than subgradient-based strategies.
  arXiv  Detail & Related papers  (2025-03-16T11:05:51Z)
• Learning Joint Models of Prediction and Optimization [56.04498536842065]
  Predict-Then-Then framework uses machine learning models to predict unknown parameters of an optimization problem from features before solving. This paper proposes an alternative method, in which optimal solutions are learned directly from the observable features by joint predictive models.
  arXiv  Detail & Related papers  (2024-09-07T19:52:14Z)
• Enhanced Bayesian Optimization via Preferential Modeling of Abstract Properties [49.351577714596544]
  We propose a human-AI collaborative Bayesian framework to incorporate expert preferences about unmeasured abstract properties into surrogate modeling. We provide an efficient strategy that can also handle any incorrect/misleading expert bias in preferential judgments.
  arXiv  Detail & Related papers  (2024-02-27T09:23:13Z)
• 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)
• Differentiable Multi-Target Causal Bayesian Experimental Design [43.76697029708785]
  We introduce a gradient-based approach for the problem of Bayesian optimal experimental design to learn causal models in a batch setting. Existing methods rely on greedy approximations to construct a batch of experiments. We propose a conceptually simple end-to-end gradient-based optimization procedure to acquire a set of optimal intervention target-state pairs.
  arXiv  Detail & Related papers  (2023-02-21T11:32:59Z)
• Joint Entropy Search for Multi-objective Bayesian Optimization [0.0]
  We propose a novel information-theoretic acquisition function for BO called Joint Entropy Search. We showcase the effectiveness of this new approach on a range of synthetic and real-world problems in terms of the hypervolume and its weighted variants.
  arXiv  Detail & Related papers  (2022-10-06T13:19:08Z)
• Uncertainty-Aware Search Framework for Multi-Objective Bayesian Optimization [40.40632890861706]
  We consider the problem of multi-objective (MO) blackbox optimization using expensive function evaluations. We propose a novel uncertainty-aware search framework referred to as USeMO to efficiently select the sequence of inputs for evaluation.
  arXiv  Detail & Related papers  (2022-04-12T16:50:48Z)
• A Robust Multi-Objective Bayesian Optimization Framework Considering Input Uncertainty [0.0]
  In real-life applications like engineering design, the designer often wants to take multiple objectives as well as input uncertainty into account. We introduce a novel Bayesian optimization framework to efficiently perform multi-objective optimization considering input uncertainty.
  arXiv  Detail & Related papers  (2022-02-25T17:45:26Z)
• Learning Proximal Operators to Discover Multiple Optima [66.98045013486794]
  We present an end-to-end method to learn the proximal operator across non-family problems. We show that for weakly-ized objectives and under mild conditions, the method converges globally.
  arXiv  Detail & Related papers  (2022-01-28T05:53:28Z)
• Leveraging Trust for Joint Multi-Objective and Multi-Fidelity Optimization [0.0]
  This paper investigates a novel approach to Bayesian multi-objective and multi-fidelity (MOMF) optimization. We suggest the innovative use of a trust metric to support simultaneous optimization of multiple objectives and data sources. Our methods offer broad applicability in solving simulation problems in fields such as plasma physics and fluid dynamics.
  arXiv  Detail & Related papers  (2021-12-27T20:55:26Z)
• Constrained multi-objective optimization of process design parameters in settings with scarce data: an application to adhesive bonding [48.7576911714538]
  Finding the optimal process parameters for an adhesive bonding process is challenging. Traditional evolutionary approaches (such as genetic algorithms) are then ill-suited to solve the problem. In this research, we successfully applied specific machine learning techniques to emulate the objective and constraint functions.
  arXiv  Detail & Related papers  (2021-12-16T10:14:39Z)
• Multi-Objective Constrained Optimization for Energy Applications via Tree Ensembles [55.23285485923913]
  Energy systems optimization problems are complex due to strongly non-linear system behavior and multiple competing objectives. In some cases, proposed optimal solutions need to obey explicit input constraints related to physical properties or safety-critical operating conditions. This paper proposes a novel data-driven strategy using tree ensembles for constrained multi-objective optimization of black-box problems.
  arXiv  Detail & Related papers  (2021-11-04T20:18:55Z)
• Runtime Analysis of Single- and Multi-Objective Evolutionary Algorithms for Chance Constrained Optimization Problems with Normally Distributed Random Variables [11.310502327308575]
  We study the scenario of components that are independent and normally distributed. We introduce a multi-objective formulation of the problem which trades off the expected cost and its variance. We prove that this approach can also be used to compute a set of optimal solutions for the chance constrained minimum spanning tree problem.
  arXiv  Detail & Related papers  (2021-09-13T09:24:23Z)
• Batched Data-Driven Evolutionary Multi-Objective Optimization Based on Manifold Interpolation [6.560512252982714]
  We propose a framework for implementing batched data-driven evolutionary multi-objective optimization. It is so general that any off-the-shelf evolutionary multi-objective optimization algorithms can be applied in a plug-in manner. Our proposed framework is featured with a faster convergence and a stronger resilience to various PF shapes.
  arXiv  Detail & Related papers  (2021-09-12T23:54:26Z)
• An Empirical Study of Assumptions in Bayesian Optimisation [61.19427472792523]
  In this work we rigorously analyse conventional and non-conventional assumptions inherent to Bayesian optimisation. We conclude that the majority of hyper- parameter tuning tasks exhibit heteroscedasticity and non-stationarity. We hope these findings may serve as guiding principles, both for practitioners and for further research in the field.
  arXiv  Detail & Related papers  (2020-12-07T16:21:12Z)
• Empirical Study on the Benefits of Multiobjectivization for Solving Single-Objective Problems [0.0]
  Local optima are often preventing algorithms from making progress and thus pose a severe threat. With the use of a sophisticated visualization technique based on the multi-objective gradients, the properties of the arising multi-objective landscapes are illustrated and examined. We will empirically show that the multi-objective COCO MOGSA is able to exploit these properties to overcome local traps.
  arXiv  Detail & Related papers  (2020-06-25T14:04:37Z)
• Incorporating Expert Prior Knowledge into Experimental Design via Posterior Sampling [58.56638141701966]
  Experimenters can often acquire the knowledge about the location of the global optimum. It is unknown how to incorporate the expert prior knowledge about the global optimum into Bayesian optimization. An efficient Bayesian optimization approach has been proposed via posterior sampling on the posterior distribution of the global optimum.
  arXiv  Detail & Related papers  (2020-02-26T01:57:36Z)

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.