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Life sciences · Preprint
arXiv · September 9, 2026
Posted before peer review. The findings may change or fail to hold.
This preprint describes X-DTC-GL, a novel DIRECT-type global optimization algorithm that combines dynamic partitioning and hill-climbing hybridization. Reported benchmarking shows ~12% improvement in solvability and ~27% in solution quality versus existing DIRECT variants; however, the work is unrefereed, applies only to synthetic optimization benchmarks, and lacks independent validation.
Comparative algorithmic benchmark study. Intervention: X-DTC-GL algorithm with dynamic partitioning and hill-climbing hybridization. Compared with: Existing DIRECT-type algorithms.
X-DTC-GL achieved improvements of ~12% in solvability versus existing DIRECT-type baselines Solution quality improvement of ~27% reported across benchmark suites Fastest convergence on up to ~40% of instances; best runtime on ~17% of problems
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This is an unrefereed arXiv preprint presenting a novel algorithmic approach with benchmark comparisons; it has not undergone peer review and cannot be evaluated for clinical or regulatory practice impact.
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The DIRECT algorithm is a deterministic global optimization method known for its versatility and balanced exploration-exploitation strategy. However, DIRECT-type algorithms are primarily effective for low-dimensional problems and often exhibit slow convergence as dimensionality increases, limiting their applicability to more complex optimization tasks. To address this limitation, this paper introduces X-DTC-GL, a novel DIRECT-type algorithm that incorporates dynamic partitioning and hybridization techniques. The dynamic partitioning approach adaptively refines the search space based on local one-dimensional surrogate models, enabling rapid subdivision of promising hyper-rectangles. The hybridization strategy selectively employs a hill-climbing method to exploit promising regions identified by the surrogate models. Extensive experiments on four diverse benchmark suites demonstrate that X-DTC-GL significantly outperforms existing DIRECT-type baselines, achieving improvements of ~12% in solvability and ~27% in solution quality. Performance-profile analyses indicate the fastest convergence on up to ~40% of instances, the best runtime performance on ~17% of problems, and competitive overall execution times. By improving performance within the partition-based framework, these advances strengthen the algorithm's competitiveness in state-of-the-art black-box optimization.
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