Jun 2, 2019 · Bayesian optimization methodologies fit a surrogate model (typically Kriging or a Gaussian Process) on evaluations of the objective function(s).

Feb 19, 2021 · Therefore, Bayesian optimization methodologies replace a single optimization of the objective function by a sequence of optimization problems: ...

Bayesian optimization is a metamodel based global optimization approach that balances exploration and exploitation. Bayesian optimization has been widely used ...

Many science, engineering, and design optimization problems require balancing the trade-offs between several conflicting objectives.

Nov 13, 2024 · We optimized the dimensions and stability of the melt pool and evaluated different objective functions and approaches using a Response Surface Model (RSM).

Book. High-Performance Simulation-Based Optimization. Title. Multi-objective Bayesian optimization for engineering simulation. Publication type. Book chapter ...

Jun 20, 2024 · Preferential Bayesian optimization (PBO) is a framework for optimizing a decision-maker's latent preferences over available design choices.

The challenge of identifying optimal trade-offs between multiple complex objective functions is pervasive in many fields, including machine learning [Sener and ...

In this paper, we propose a novel multi-objective optimization framework based on hierarchical Bayesian optimization and agent-based modeling (Hierarchical-PABO) ...

Jun 24, 2024 · We here examine a cooperative approach where both the designer and optimization process share a common goal and work in partnership.