{
  "abstract": "Collaborative Attention Ant Colony Optimization (CAACO) integrates a deterministic coordinate-based attention prior with subpath decomposition, memetic MAX–MIN Ant System search, and double-bridge perturbation for the symmetric Euclidean Traveling Salesman Problem. The attention prior combines coordinate-direction compatibility and distance information to guide candidate selection without model training. Random-offset slicing organizes endpoint-constrained subpath search, while local improvement and perturbation refine and diversify the tour. An exploratory comparison of Full CAACO and CAACO-KNN uses 30 recorded runs per configuration on each of eight EUC_2D benchmark instances. Average instance-level mean GAP values are 2.945% and 3.222%, respectively, with a lower mean for Full CAACO on six instances. A two-sided exact Wilcoxon signed-rank test over the eight instance means gives p=0.0391; four per-instance comparisons remain significant after Holm correction. The results characterize configuration-level performance on the selected benchmarks and motivate further controlled evaluation of attention-guided search.",
  "abstract_shown": true,
  "affiliations": [
    "School of Intelligent Systems Science and Engineering/JNU-Industry School of Artificial Intelligence, Jinan University, Guangzhou 510006, China",
    "ERICA Campus, Hanyang University, Ansan 15588, Republic of Korea"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Kexin Zhang"
    },
    {
      "affs": [
        0
      ],
      "name": "Shubin Liu"
    },
    {
      "affs": [
        0
      ],
      "name": "Xinyuan Zhang"
    },
    {
      "affs": [
        1
      ],
      "name": "Jun Zhang",
      "orcid": "0000-0001-7835-9871"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/14anaa0ycqpe1.bib",
  "cited_by": 0,
  "doi": "10.3390/math14203815",
  "fulltext_url": null,
  "id": "14anaa0ycqpe1",
  "issns": [
    "2227-7390"
  ],
  "landing_url": "https://www.mdpi.com/2227-7390/14/20/3815",
  "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",
      "1702|Metaheuristic Optimization Algorithms Research"
    ]
  ],
  "openalex": "W7202173104",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-20",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/14anaa0ycqpe1.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:29Z",
      "native_id": "10.3390/math14203815",
      "source": "crossref",
      "url": "https://www.mdpi.com/2227-7390/14/20/3815"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "issn:2227-7390",
      "subfield": "general"
    }
  ],
  "title": "Collaborative Attention Ant Colony Optimization for Large-Scale Traveling Salesman Problems",
  "title_text": "Collaborative Attention Ant Colony Optimization for Large-Scale Traveling Salesman Problems",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:18:29Z",
  "url": "https://research.draconitic.com/paper/14anaa0ycqpe1",
  "venue": "Mathematics",
  "versions": []
}