{
  "abstract": "This paper proposes an improved Artificial Hummingbird Algorithm (AHA) for solving numerical optimization problems. The greedy Fitness-Distance Balance (FDB) selection method is integrated into the guided, territorial, and migration search operators of the AHA. This establishes a balance between exploration and exploitation by enhancing AHA's exploration capabilities. The performance of the proposed FDB-AHA is evaluated using CEC 2017 and 2020 problems which include single-mode, multi-mode, hybrid, and composition functions in search spaces of dimensions 30, 50, and 100. Furthermore, the results of the FDB-AHA are compared with 11 other advanced search algorithms using the Friedman statistical test method on the CEC 2017 and 2020 problems. As a result of these comparisons, the FDB-AHA was found to be the most effective method, while the AHA ranked ninth. These findings confirm the effectiveness of the improvements made in the AHA. Moreover, the FDB-AHA has been used to determine the optimal solutions for load frequency control in three different power systems. In this context, three-term (PID), five-term (FOPID), and six-term (FOPI-FOPD) controllers are optimized and compared with existing studies in the literature for time-domain analysis. Furthermore, load variation and robustness analysis are also performed. Finally, the practicality of the controllers optimized by FDB-AHA is verified using the real-time simulator OP5707. Simulation results show that FDB-AHA is a highly efficient algorithm for solving all optimization problems.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Energy Systems Engineering, Karadeniz Technical University, 61830 Trabzon, Türkiye.",
    "Bulancak Vocational School, Giresun University, 28300 Giresun, Türkiye."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Tural Aslanov",
      "orcid": "0000-0002-4248-896X"
    },
    {
      "affs": [
        0
      ],
      "name": "Bora Çavdar",
      "orcid": "0000-0002-0545-2925"
    },
    {
      "affs": [
        0
      ],
      "name": "Ömür Akyazı",
      "orcid": "0000-0001-6266-2323"
    },
    {
      "affs": [
        1
      ],
      "name": "Kenan Yanmaz",
      "orcid": "0000-0002-6384-293X"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/d2qwhars8dakj.bib",
  "cited_by": 0,
  "doi": "10.3390/biomimetics11100754",
  "fulltext_url": "https://europepmc.org/articles/PMC13702298",
  "id": "d2qwhars8dakj",
  "issns": [
    "2313-7673"
  ],
  "landing_url": "https://europepmc.org/article/MED/42930306",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1702|Metaheuristic Optimization Algorithms Research"
    ]
  ],
  "openalex": "W7222933061",
  "pdf_url": "https://europepmc.org/articles/PMC13702298?pdf=render",
  "pmcid": "PMC13702298",
  "pmid": "42930306",
  "published": "2026-10-19",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/d2qwhars8dakj.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:22:01Z",
      "native_id": "MED:42930306",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42930306"
    }
  ],
  "subjects": [],
  "title": "Improvement of Artificial Hummingbird Algorithm with Greedy Fitness-Distance Balance Method and Its Application to Load Frequency Control.",
  "title_text": "Improvement of Artificial Hummingbird Algorithm with Greedy Fitness-Distance Balance Method and Its Application to Load Frequency Control.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:22:01Z",
  "url": "https://research.draconitic.com/paper/d2qwhars8dakj",
  "venue": "Biomimetics (Basel, Switzerland)",
  "versions": []
}