Bayesian optimization as a flexible and efficient design framework for
sustainable process systems
- URL: http://arxiv.org/abs/2401.16373v1
- Date: Mon, 29 Jan 2024 18:12:32 GMT
- Title: Bayesian optimization as a flexible and efficient design framework for
sustainable process systems
- Authors: Joel A. Paulson and Calvin Tsay
- Abstract summary: We provide an overview of recent developments, challenges, and opportunities in BO for design of next-generation process systems.
After describing several motivating applications, we discuss how advanced BO methods have been developed to more efficiently tackle important problems in these applications.
- Score: 2.7059126618449527
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Bayesian optimization (BO) is a powerful technology for optimizing noisy
expensive-to-evaluate black-box functions, with a broad range of real-world
applications in science, engineering, economics, manufacturing, and beyond. In
this paper, we provide an overview of recent developments, challenges, and
opportunities in BO for design of next-generation process systems. After
describing several motivating applications, we discuss how advanced BO methods
have been developed to more efficiently tackle important problems in these
applications. We conclude the paper with a summary of challenges and
opportunities related to improving the quality of the probabilistic model, the
choice of internal optimization procedure used to select the next sample point,
and the exploitation of problem structure to improve sample efficiency.
Related papers
- ADO-LLM: Analog Design Bayesian Optimization with In-Context Learning of Large Language Models [5.642568057913696]
This paper presents ADO-LLM, the first work integrating large language models (LLMs) with Bayesian Optimization for analog design optimization.
ADO-LLM leverages the LLM's ability to infuse domain knowledge to rapidly generate viable design points to remedy BO's inefficiency in finding high value design areas.
We evaluate the proposed framework on two different types of analog circuits and demonstrate notable improvements in design efficiency and effectiveness.
arXiv Detail & Related papers (2024-06-26T21:42:50Z) - Diffusion Model for Data-Driven Black-Box Optimization [54.25693582870226]
We focus on diffusion models, a powerful generative AI technology, and investigate their potential for black-box optimization.
We study two practical types of labels: 1) noisy measurements of a real-valued reward function and 2) human preference based on pairwise comparisons.
Our proposed method reformulates the design optimization problem into a conditional sampling problem, which allows us to leverage the power of diffusion models.
arXiv Detail & Related papers (2024-03-20T00:41:12Z) - Reinforced In-Context Black-Box Optimization [64.25546325063272]
RIBBO is a method to reinforce-learn a BBO algorithm from offline data in an end-to-end fashion.
RIBBO employs expressive sequence models to learn the optimization histories produced by multiple behavior algorithms and tasks.
Central to our method is to augment the optimization histories with textitregret-to-go tokens, which are designed to represent the performance of an algorithm based on cumulative regret over the future part of the histories.
arXiv Detail & Related papers (2024-02-27T11:32:14Z) - 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) - Rethinking and Benchmarking Predict-then-Optimize Paradigm for
Combinatorial Optimization Problems [62.25108152764568]
Many web applications rely on solving optimization problems, such as energy cost-aware scheduling, budget allocation on web advertising, and graph matching on social networks.
We consider the performance of prediction and decision-making in a unified system.
We provide a comprehensive categorization of current approaches and integrate existing experimental scenarios.
arXiv Detail & Related papers (2023-11-13T13:19:34Z) - 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) - Transfer Learning for Bayesian Optimization: A Survey [29.229660973338145]
Black-box optimization is a powerful tool that models and optimize such expensive "black-box" functions.
Researchers in the BO community propose to incorporate the spirit of transfer learning to accelerate optimization process.
arXiv Detail & Related papers (2023-02-12T14:37:25Z) - An Interactive Knowledge-based Multi-objective Evolutionary Algorithm
Framework for Practical Optimization Problems [5.387300498478744]
This paper proposes an interactive knowledge-based evolutionary multi-objective optimization (IK-EMO) framework.
It extracts hidden variable-wise relationships as knowledge from evolving high-performing solutions, shares them with users to receive feedback, and applies them back to the optimization process to improve its effectiveness.
The working of the proposed IK-EMO is demonstrated on three large-scale real-world engineering design problems.
arXiv Detail & Related papers (2022-09-18T16:51:01Z) - Machine Learning Framework for Quantum Sampling of Highly-Constrained,
Continuous Optimization Problems [101.18253437732933]
We develop a generic, machine learning-based framework for mapping continuous-space inverse design problems into surrogate unconstrained binary optimization problems.
We showcase the framework's performance on two inverse design problems by optimizing thermal emitter topologies for thermophotovoltaic applications and (ii) diffractive meta-gratings for highly efficient beam steering.
arXiv Detail & Related papers (2021-05-06T02:22:23Z) - Multi-Objective Optimization of the Textile Manufacturing Process Using
Deep-Q-Network Based Multi-Agent Reinforcement Learning [5.900286890213338]
The paper proposes a multi-agent reinforcement learning (MARL) framework to transform the optimization process into a game.
A utilitarian selection mechanism was employed in the game to avoid the interruption of multiple equilibriumlibria.
The proposed MARL system is possible to achieve the optimal solutions for the textile ozonation process and it performs better than the traditional approaches.
arXiv Detail & Related papers (2020-12-02T11:37:44Z) - Automatically Learning Compact Quality-aware Surrogates for Optimization
Problems [55.94450542785096]
Solving optimization problems with unknown parameters requires learning a predictive model to predict the values of the unknown parameters and then solving the problem using these values.
Recent work has shown that including the optimization problem as a layer in a complex training model pipeline results in predictions of iteration of unobserved decision making.
We show that we can improve solution quality by learning a low-dimensional surrogate model of a large optimization problem.
arXiv Detail & Related papers (2020-06-18T19:11:54Z)
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.