← Back to all news
arXiv Machine Learning By Qineng Wang, Zhendong Guo, Yun Chen, Guangjian Ma, Liming Song, Jun Li

A Dynamic Aggregation Strategy Enhanced Efficient Global Optimization Algorithm for Solving High-Dimensional Turbomachinery Design Problems

Read the original on arXiv Machine Learning →

The paper introduces DA‑EGO, an efficient global optimization algorithm that dynamically aggregates high‑dimensional design spaces into low‑dimensional subspaces for surrogate‑based search. The algorithm updates subspace variables each iteration using variable‑interaction analyses, perturbation, and ANOVA, and adaptively adjusts search ranges based on previous results. Tests on 21 benchmark functions and real turbomachinery problems demonstrate DA‑EGO’s effectiveness, especially on separable and partially separable problems, while noting case‑dependent performance on non‑separable functions.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv Machine Learning
Sep 24

tidyHEBO: Robust General-Purpose Bayesian Optimization with Model-Consistent Warping and Pareto Search

tidyHEBO is a BoTorch-native Bayesian optimization tool that jointly applies Yeo-Johnson output warping to a Gaussian‑process surrogate, evaluates acquisition functions on the original objective scale, and conducts constrained cumulative Pareto search across multiple acquisition criteria. Using only default settings, it outperformed other methods on the Olympus benchmark and performed strongly on synthetic, Needle‑in‑a‑Haystack, and Bayesmark tasks, while adaptive batching offered a trade‑off between parallelization and optimization quality. These results position tidyHEBO as a robust, reproducible optimizer suitable for diverse practical problems, including scientific applications and hyperparameter tuning.

By L. A. Zhukov, E. V. Shaburova, D. V. Antonets
benchmarks
More like this →
arXiv Machine Learning
Jul 16

Power Homotopy for Zeroth-Order Non-Convex Optimizations

arXiv:2511. 13592v2 Announce Type: replace-cross Abstract: The existing method of GS-PowerOpt solves the non-convex optimization problem of the form $\max_{\boldsymbol{x} \in \mathbb{R}^d} f(\boldsymbol{x})$ through maximizing a Gaussian-smoothed surrogate $F_{N,\sigma}(\boldsymbol{\mu}) = \mathbb{E}_{\boldsymbol{x}\sim\mathcal{N}(\boldsymbol{\mu},\sigma^2 I_d)}[e^{N f(\boldsymbol{x})}]$.

By Chen Xu
safety
More like this →
arXiv Machine Learning
Aug 5

Exploiting Separability in Multi-Scale Grey-Box Bayesian Optimization

arXiv:2608. 03045v1 Announce Type: new Abstract: We consider grey-box optimization problems where the decision variables naturally partition into black-box variables (as arguments to an expensive black-box function) and white-box variables, governed by a set of explicit, closed-form equations that also depend on the output of the black-box function.

By Joshua E. Hammond, Tyler A. Soderstrom, Brian A. Korgel, Michael Baldea
benchmarks
More like this →