A generalizable transformation methodology is developed that converts compatible single- or multi-objective sequential Bayesian optimization methods into B-MOBO methods that are as effective as existing methods at solving optimization problems in terms of solution quality and show improvements when real-time is considered.
Abstract
Bayesian optimization is a surrogate-based global optimization method that is increasingly being used for engineering design. However, most methods are designed to be sequential, and in optimization problems where the functional evaluation can be easily parallelized, batch methods are more effective at reducing real-time computation. While existing batch multi-objective Bayesian optimization (B-MOBO) methods achieve strong performance, they are typically purpose-built from scratch, limiting ability to leverage the extensive library of proven sequential acquisition functions for parallel settings. In this paper, a generalizable transformation methodology is developed that converts compatible single- or multi-objective sequential Bayesian optimization methods into B-MOBO methods. The key innovation is a Euclidean distance-based composite acquisition function with multi-objective penalization averaging, which combines multiple sequential acquisition functions while incorporating objective-wise penalization information from all objectives and preventing redundant sampling in batch selection. To demonstrate the schema, this methodology is applied to two representative sequential acquisition strategies, the expected improvement method (single-objective) and the quality metrics method (multi-objective), to develop two new B-MOBO variants. These new methods are then compared against their sequential counterparts and established B-MOBO methods using both computation time and real-time analysis. Results show that the new B-MOBO methods are as effective as existing methods at solving optimization problems in terms of solution quality and show improvements when real-time is considered.
A unified framework, KENDO (Kernel ENsemble Disagreement-aware Operator), is proposed that integrates Ensemble Gaussian Processes (EGP) with disagreement-aware acquisition strategies and extends the approach to multi-objective optimization via random scalarization that preserves the single-optimizer conditioning structure.
Heng Zhang, Haotian Xiang, Qin Lu et al.· 0 citations
BOCoDe is introduced, an open-source, PyTorch-native benchmark comprising 307 BBO problems, including 159 engineering design tasks and widely used synthetic and HPO benchmarks that establish a reproducible and extensible foundation for developing and evaluating BO methods that better reflect the demands of engineering design.
Rosen Yu, Christophe Hatterer, A. Narayanan et al.· 0 citations
This study introduces a novel constrained multi-objective evolutionary algorithm, termed DPCME, which employs two interacting populations that exchange information, enabling effective global exploration and reducing the risk of convergence to local optima.
This work presents a surrogate-assisted multi-objective optimization approach that leverages symbolic regression (SR) to construct interpretable analytical approximations of expensive black-box functions. It uses iterative construction of surrogates (symbolic models) and Tchebycheff scalarization along a set of parallel reference vectors to search for well-distributed solutions on the Pareto front. Optimization of the symbolic models is performed using a nonlinear programming (NLP) solver in lieu of heuristic search. A distance-based subset selection strategy is used to select a candidate for true evaluation, ensuring efficient use of limited evaluation budget in a steady-state framework. The approach is compared against a Kriging-assisted NSGA-II, also implemented within a steady-state framework. Experiments are performed on six benchmark problems—covering both constrained and unconstrained cases, including a practical engineering benchmark of bracket design problem. The findings highlight the strengths and limitations of SR-based surrogates combined with NLP, and position them as a viable alternative to conventional surrogate-assisted evolutionary algorithms.
Kannan Sekar, H. Singh, Tapabrata Ray· Proceedings of the Genetic a...· 0 citations
Multi-objective Bayesian optimization (MOBO) is effective in identifying the Pareto fronts for expensive black-box problems. However, most current MOBO approaches are limited to low-dimensional decision space due to its exponential sampling complexity. This paper presents decision variable interaction analysis-based MOBO, ViaMOBO, a generic framework for expensive multi-objective problems with high-dimensional decision space. The key idea of ViaMOBO is that it utilizes a variable interaction analysis model to determine whether the decision space can be completely or partially divided, and then performs local Bayesian optimization in the divided decision subspaces. Through the variable analysis model, it can be derived whether the objectives in black-box problems are separable, partially separable, or non-separable based on the potential independent or interdependent relationships among decision variables without any strong assumptions. We compare ViaMOBO with the state-of-the-art MOBO methods on both synthetic and real-world benchmarks. The experimental results demonstrate that ViaMOBO outperforms other related MOBO baselines in approximating the Pareto front of high-dimensional expensive multi-objective problems.
A surrogate-recommendation framework is introduced that predicts the most suitable BO surrogate from inexpensive dataset characteristics and establishes FruBO as a reproducible, compute-aware baseline for Bayesian Optimization and provides practical guidance for surrogate selection under limited computational and experimental budgets.
P. Krokidas, C. Rekatsinas, Vassilis Sioros et al.· 1 citation