{
  "abstract": "Numerous real-world applications involve large-scale multi-objective optimization problems (LSMOPs) with hundreds or even thousands of decision variables. Although multi-objective evolutionary algorithms (MOEAs) are effective for such tasks, relying solely on objective-space information often provides insufficient selection pressure in high-dimensional settings. To address this issue, an adaptive dual-space discriminator is proposed to coordinate selection strategies from both the objective and decision spaces through a state-aware adaptive operator-selection method with a recurrent selector based on direct population-improvement feedback, and a corresponding algorithm termed MOEA-RLD is developed by integrating dual-space selection, online strategy adaptation, a bounded population pool, and APD environmental selection. Comparative evaluations on the LSMOP benchmark suite against five representative large-scale multi-objective evolutionary algorithms show that MOEA-RLD achieves favorable average rankings across the tested settings. Further evaluations on three real-world application settings provide evidence of both the practical performance of the proposed method and its limitations.",
  "abstract_shown": true,
  "affiliations": [
    "College of Computer Science and Artificial Intelligence, Southwest Minzu University"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Ziyi Xue"
    },
    {
      "affs": [
        0
      ],
      "name": "Kaidong Cheng"
    },
    {
      "affs": [
        0
      ],
      "name": "Xunze Liu"
    },
    {
      "affs": [
        0
      ],
      "name": "Yingjie Li"
    },
    {
      "affs": [
        0
      ],
      "name": "Gang Xin"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1dpsqz0qn97k4.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-70129-w",
  "fulltext_url": "https://doi.org/10.1038/s41598-026-70129-w",
  "id": "1dpsqz0qn97k4",
  "issns": [
    "2045-2322"
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  "landing_url": "https://doi.org/10.1038/s41598-026-70129-w",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "Q"
    ],
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      "lcc",
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    [
      "openalex",
      "1703|Advanced Multi-Objective Optimization Algorithms"
    ]
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  "openalex": "W7212390208",
  "pdf_url": "https://www.nature.com/articles/s41598-026-70129-w_reference.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-01",
  "publisher": "Nature Portfolio",
  "ris": "https://research.draconitic.com/paper/1dpsqz0qn97k4.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:45:52Z",
      "native_id": "08a1d6cd73214266b6a7b29ea3d6ceaa",
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      "url": "https://doi.org/10.1038/s41598-026-70129-w"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "lcc:R",
      "subfield": "general"
    }
  ],
  "title": "Adaptive dual-space discriminator for large-scale multi-objective optimization",
  "title_text": "Adaptive dual-space discriminator for large-scale multi-objective optimization",
  "type": "peer-reviewed",
  "updated_at": "2026-10-31T14:20:43Z",
  "url": "https://research.draconitic.com/paper/1dpsqz0qn97k4",
  "venue": "Scientific Reports",
  "versions": []
}