{
  "abstract": "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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "Journal reference: Applied Soft Computing, 2026, 116392",
  "arxiv": "2609.09796",
  "author_total": 2,
  "authors": [
    {
      "name": "Linas Stripinis"
    },
    {
      "name": "Remigijus Paulavičius"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9ecxgzz02yrb4.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.09796",
  "fulltext_url": null,
  "id": "9ecxgzz02yrb4",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.09796",
  "language": null,
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "cs.NA"
    ],
    [
      "arxiv",
      "math.NA"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2609.09796",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-09",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/9ecxgzz02yrb4.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:21:29Z",
      "native_id": "2609.09796",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.09796"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.NA",
      "subfield": "numerical-analysis"
    }
  ],
  "title": "A practical DIRECT-type algorithm for medium-scale black-box global optimization",
  "title_text": "A practical DIRECT-type algorithm for medium-scale black-box global optimization",
  "type": "preprint",
  "updated_at": "2026-10-30T12:21:29Z",
  "url": "https://research.draconitic.com/paper/9ecxgzz02yrb4",
  "venue": "arXiv",
  "versions": [
    {
      "doi": "10.1016/j.asoc.2026.116392",
      "pid": null,
      "relation": "published-as",
      "title": null,
      "venue": null
    }
  ]
}