Vol. 1 No. 4 (2026)

Articles

Knowledge-Transfer Surrogate Search for Expensive Large-Scale Black-Box Optimization Problems

Published 2026-08-07

  • Greta Frank
    • ,
  • Mia Koch

Greta Frank
Chair of Architectural Informatics, Department of Architecture, School of Engineering and Design, Technical University of Munich, 80333 Munich, Germany

Mia Koch
Chair of Architectural Informatics, Department of Architecture, School of Engineering and Design, Technical University of Munich, 80333 Munich, Germany

Artificial Intelligence and Intelligent Systems Research, Volume 1, Issue 4, 2026 cover

Abstract

The optimization of expensive large-scale black-box problems presents a significant challenge in computational science and engineering. As objective evaluations often rely on time-consuming simulations, such as computational fluid dynamics or finite element analysis, the total number of feasible evaluations is strictly limited. Surrogate-assisted optimization algorithms have been proposed to alleviate this computational burden by constructing data-driven approximation models. However, in large-scale search spaces, the volume of data required to build an accurate surrogate model grows exponentially, rendering conventional cold-start approaches highly inefficient. To address this fundamental limitation, this paper proposes a comprehensive knowledge-transfer surrogate search methodology. By systematically extracting and adapting historical optimization data from previously solved source tasks, the proposed framework accelerates the initial construction and subsequent refinement of surrogate models in the target task domain. We introduce a robust domain adaptation mechanism to align the structural characteristics of heterogeneous search spaces, coupled with a transfer-aware acquisition function that dynamically balances the exploration of the target space with the exploitation of transferred source knowledge. Extensive theoretical formulations and methodological frameworks are provided to establish the reliability of the knowledge transfer process. Furthermore, comprehensive empirical evaluations across a wide spectrum of benchmark suites demonstrate that the proposed approach substantially reduces the required number of objective evaluations while simultaneously improving the final convergence quality compared to traditional optimization techniques. Keywords: Black-Box Optimization, Surrogate Models, Knowledge Transfer, Domain Adaptation.

Keywords

Surrogate-Assisted Optimization, Knowledge Transfer, Large-Scale Optimization, Expensive Black-Box Problems, Evolutionary Search

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