{
  "abstract": "Heavy-tailed perturbations are commonly used in differential evolution (DE) to restore diversity and mitigate premature convergence. Adaptive Cauchy perturbation refines this strategy by varying perturbation probabilities according to dimension-wise convergence, but it retains a fixed Cauchy law and cannot adjust perturbation severity across individuals. This paper proposes rank-conditioned α-stable perturbation, a two-level adaptive operator that decouples perturbation activation from tail-shape control. Dimension-wise convergence indicators determine coordinate-specific perturbation probabilities, while each individual’s fitness rank determines the stability index of a symmetric α-stable kernel. Thus, higher-ranked individuals receive lighter-tailed perturbations for local refinement, whereas lower-ranked individuals receive heavier-tailed perturbations for occasional long-range exploration. Unlike fixed-kernel heavy-tailed DE operators, the proposed operator induces a per-individual exploration–exploitation trade-off within each generation. It is embedded into L-SRTDE, the winner of the IEEE CEC 2024 competition, and evaluated on the IEEE CEC 2017 benchmark suite across multiple dimensions. Results show the best Friedman rankings among the compared methods and favorable Wilcoxon outcomes against recent DE variants. Controlled comparisons with conventional Cauchy and adaptive dimension-wise Cauchy perturbations show that rank-guided tail-shape adaptation complements probability-based activation, with modest overhead and stable performance across α-stable parameter settings.",
  "abstract_shown": true,
  "affiliations": [
    "Graduate School of Data Science, Chonnam National University, Gwangju, Republic of Korea",
    "Department of Lifelong Education, Kyungil University, Gyeongsangbuk-do, Republic of Korea"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Tae Jong Choi"
    },
    {
      "affs": [
        1
      ],
      "name": "Yeji An"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2ydwfw9abegq3.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0357907",
  "fulltext_url": null,
  "id": "2ydwfw9abegq3",
  "issns": [
    "1932-6203"
  ],
  "landing_url": "https://doi.org/10.1371/journal.pone.0357907",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1702|Metaheuristic Optimization Algorithms Research"
    ],
    [
      "plos",
      "/Biology and life sciences/Biochemistry/Proteins/Immune system proteins/Complement system/Complement activation"
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      "/Biology and life sciences/Evolutionary biology/Evolutionary processes/Convergent evolution"
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      "/Biology and life sciences/Immunology/Immune system proteins/Complement system/Complement activation"
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    ],
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      "plos",
      "/Medicine and health sciences/Immunology/Immune system proteins/Complement system/Complement activation"
    ],
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      "plos",
      "/Medicine and health sciences/Immunology/Immune system/Complement system/Complement activation"
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      "/Physical sciences/Mathematics/Optimization"
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      "/Physical sciences/Mathematics/Probability theory/Probability distribution"
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      "/Research and analysis methods/Computational techniques/Evolutionary computation/Evolutionary algorithms"
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    [
      "plos",
      "/Research and analysis methods/Simulation and modeling/Algorithms"
    ],
    [
      "plos",
      "/Research and analysis methods/Simulation and modeling/Algorithms/Evolutionary algorithms"
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  "openalex": "W7212059906",
  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0357907&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-09",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/2ydwfw9abegq3.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:56Z",
      "native_id": "10.1371/journal.pone.0357907",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0357907"
    }
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  "subjects": [
    {
      "field": "biology",
      "origin": "plos:immunology",
      "subfield": "immunology"
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    {
      "field": "medicine",
      "origin": "plos:physiology",
      "subfield": "physiology"
    }
  ],
  "title": "A rank-conditioned α-stable perturbation for differential evolution: Separating perturbation frequency and tail heaviness",
  "title_text": "A rank-conditioned α-stable perturbation for differential evolution: Separating perturbation frequency and tail heaviness",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:56Z",
  "url": "https://research.draconitic.com/paper/2ydwfw9abegq3",
  "venue": "PLOS One",
  "versions": []
}