{
  "abstract": "The sea surface temperature sparse sensor placement (SST-SSP) problem involves selecting a limited number of in situ observation sites to reconstruct full sea surface temperature fields. We first establish the SST-SSP model, considering proper orthogonal decomposition (POD) information, deployment burden, and modal redundancy. This is different from well-known POD-based solutions. Then, we propose a cost-aware redundancy-suppressed multi-objective evolutionary algorithm based on decomposition (CARS-MOEA/D) to solve this problem. Specifically, CARS-MOEA/D uses redundancy-assisted decomposition search to explore complementary layouts and validation-calibrated external archiving to retain favorable reconstruction-burden trade-offs. Experiments on 36 Optimum Interpolation Sea Surface Temperature (OISST) instances cover eleven regional and basin-scale domains, as well as the global ocean at three spatial resolutions. The results demonstrate that CARS-MOEA/D outperforms state-of-the-art multi-objective evolutionary algorithms. A Tropical Atmosphere Ocean buoy-network case study further illustrates the practical value of CARS-MOEA/D. The official data sources and source-code repository are provided to support reproducible research and future work in this area.",
  "abstract_shown": true,
  "affiliations": [
    "Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Haotian Shen"
    },
    {
      "affs": [
        0
      ],
      "name": "Jinsi Zhang",
      "orcid": "0009-0008-8044-1127"
    },
    {
      "affs": [
        0
      ],
      "name": "Zuocheng Li"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/76n6sexgdz5he.bib",
  "cited_by": 0,
  "doi": "10.3390/math14193442",
  "fulltext_url": null,
  "id": "76n6sexgdz5he",
  "issns": [
    "2227-7390"
  ],
  "landing_url": "https://www.mdpi.com/2227-7390/14/19/3442",
  "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",
      "1703|Advanced Multi-Objective Optimization Algorithms"
    ]
  ],
  "openalex": "W7215105832",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-22",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/76n6sexgdz5he.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:29Z",
      "native_id": "10.3390/math14193442",
      "source": "crossref",
      "url": "https://www.mdpi.com/2227-7390/14/19/3442"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "issn:2227-7390",
      "subfield": "general"
    }
  ],
  "title": "Redundancy-Suppressed Multi-Objective Evolutionary Algorithm with Validation-Calibrated Archiving for Sea Surface Temperature Sensor Placement",
  "title_text": "Redundancy-Suppressed Multi-Objective Evolutionary Algorithm with Validation-Calibrated Archiving for Sea Surface Temperature Sensor Placement",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:18:29Z",
  "url": "https://research.draconitic.com/paper/76n6sexgdz5he",
  "venue": "Mathematics",
  "versions": []
}