{
  "abstract": "Where should multi-objective reasoning enter a swarm optimizer: before the search through scalarization, around the search through survival or archiving, or inside the search through Pareto-informed leader selection? We study this question on the Mountain Gazelle Optimizer (MGO) and propose a feedback-path taxonomy that separates the three roles. A census of twenty multi-objective MGO extensions finds one prior leader-mediated design and no method that assigns per-agent leaders from a Pareto archive. We develop four leader-mediated variants on one search skeleton, with a random-leader control and a leader-isolation ablation. On twenty-two benchmark problems (thirty runs each), all four variants rank ahead of five paper-based reconstructions of prior MGO designs on three indicators, and the decomposition and indicator leader rules outrank random leadership on hypervolume and IGD+ under Holm correction. On fourteen held-out engineering problems, a refined indicator-guided design separates from NSGA-II, NSGA-III, MOEA/D, AGE-MOEA-II and SMS-EMOA on IGD+ under returned-set scoring, and from all but SMS-EMOA on hypervolume; under evaluation-trace scoring, only from NSGA-III and MOEA/D on IGD+. In sample, of the five it separates only from MOEA/D (ε-indicator, returned-set scoring). The four variants outperform the prior MGO designs as complete implementations; against established algorithms, the advantage depends on scoring.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Computer Science, Faculty of Informatics, Mahasarakham University, Maha Sarakham 44150, Thailand"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Preut Thanarat",
      "orcid": "0009-0004-7172-0811"
    },
    {
      "affs": [
        0
      ],
      "name": "Suchart Khummanee",
      "orcid": "0000-0002-6078-1203"
    },
    {
      "affs": [
        0
      ],
      "name": "Chatklaw Jareanpon",
      "orcid": "0000-0001-8605-7144"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/777ne4bvv5rem.bib",
  "cited_by": 0,
  "doi": "10.3390/math14214043",
  "fulltext_url": null,
  "id": "777ne4bvv5rem",
  "issns": [
    "2227-7390"
  ],
  "landing_url": "https://www.mdpi.com/2227-7390/14/21/4043",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [],
  "openalex": null,
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-04",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/777ne4bvv5rem.ris",
  "sources": [
    {
      "first_seen": "2026-11-05T00:20:49Z",
      "native_id": "10.3390/math14214043",
      "source": "crossref",
      "url": "https://www.mdpi.com/2227-7390/14/21/4043"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "issn:2227-7390",
      "subfield": "general"
    }
  ],
  "title": "Pareto-Guided Leader Selection in the Mountain Gazelle Optimizer: A Controlled Multi-Objective Study",
  "title_text": "Pareto-Guided Leader Selection in the Mountain Gazelle Optimizer: A Controlled Multi-Objective Study",
  "type": "peer-reviewed",
  "updated_at": "2026-11-06T00:21:06Z",
  "url": "https://research.draconitic.com/paper/777ne4bvv5rem",
  "venue": "Mathematics",
  "versions": []
}