# KT-EGO: A Knowledge Transfer Assisted Efficient Global Optimization Algorithm for Solving High-Dimensional Expensive Black-Box Problems

arXiv: https://arxiv.org/abs/2609.37473 · HTML: https://mlsanity.com/paper/2609.37473/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 62 references that are in the map.
- First posted on arXiv 28 Sep 2026.

## Builds on (most cited first, 3 of 3)

- [Gradient-enhanced kriging for high-dimensional problems](https://mlsanity.com/paper/1708.02663/) (2017)
- [On efficient global optimization via universal Kriging surrogate models](https://mlsanity.com/paper/1803.08667/) (2018)
- [Parallel Multi-Fidelity Expected Improvement Method for Efficient Global Optimization](https://mlsanity.com/paper/2609.27328/) (2026)

## Abstract (arXiv)

Many engineering problems involve optimizing a high-dimensional expensive black-box (HEB) design space. To solve such problems efficiently, we propose a knowledge transfer assisted efficient global optimization (EGO) algorithm, labeled as KT-EGO, which extends the EGO algorithm for solving problems over higher dimensions (i.e., d>20). Specifically, the original design space is divided into several low-dimensional subset design spaces. More importantly, in order to extract information from the subset design spaces to accelerate the progress of full optimization, we propose a surrogate-based data fusion strategy in KT-EGO. And further, a searching strategy with an adaptive variable range is devised to enhance the exploitation of promising areas. To show the effectiveness of our proposed algorithm, it is compared against the state-of-the-art algorithms over 12 benchmark functions and a 28-dimensional engineering optimization for the design of compressor blade, which fully validates the effectiveness of the KT-EGO for solving HEB problems.