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Life sciences · Preprint
arXiv · August 12, 2026
Posted before peer review. The findings may change or fail to hold.
ViaMOBO is a proposed Bayesian optimization framework designed to tackle high-dimensional multi-objective black-box problems by leveraging learned variable interactions to decompose the decision space. The preprint reports superior performance over existing MOBO baselines on unspecified synthetic and real-world benchmarks, but remains unreviewed and provides no quantitative effect sizes, confidence intervals, or statistical significance tests.
Preprint. Intervention: ViaMOBO: variable interaction analysis-based multi-objective Bayesian optimization framework with decision space decomposition.. Compared with: State-of-the-art MOBO methods (specific baselines not named in abstract)..
ViaMOBO outperforms state-of-the-art MOBO methods in approximating the Pareto front of high-dimensional expensive multi-objective problems Variable interaction analysis can determine whether objectives are separable, partially separable, or non-separable without strong assumptions
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This is an unreviewed arXiv preprint presenting a novel computational method (ViaMOBO) with experimental validation on synthetic and real-world benchmarks, but lacking peer review and clinical or translational endpoints.
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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.
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