A Comprehensive Benchmark Study on Five Novel Metaheuristics: Flood, Football Training, Goat, Human Evolutionary, and Tornado-Based Optimization Approaches

Fatih Demirel; Sibel Arslan

doi:10.17798/bitlisfen.1728348

A Comprehensive Benchmark Study on Five Novel Metaheuristics: Flood, Football Training, Goat, Human Evolutionary, and Tornado-Based Optimization Approaches

Abstract

We present a unified benchmark of five recently introduced population-based metaheuristics—Flood Algorithm (FA), Football Team Training Algorithm (FTTA), Goat Optimization Algorithm (GOA), Human Evolutionary Optimization Algorithm (HEOA), and Tornado Optimization with Coriolis Force (TOC)—under strictly comparable conditions on five canonical engineering design problems (spring, welded beam, gear train, speed reducer, and pressure vessel). Each method was independently run 100 times with a population of 30 individuals and a 150-iteration budget, and performance was assessed by solution quality (best/mean), variability (std), and convergence behavior. To establish statistical robustness, we complemented pairwise t-tests with Wilcoxon signed-rank post-hoc tests under Holm correction and reported effect sizes. Results show that FTTA and FA consistently combine fast, low-variance convergence on continuous constrained designs, TOC excels in precision-sensitive/discrete settings (notably Gear Train), GOA remains competitive yet problem-dependent, and HEOA generally underperforms. Overall, superiority is problem-dependent rather than universal, aligning with the No Free Lunch perspective. Beyond aggregate rankings, the study offers practical guidance by mapping problem characteristics to algorithm strengths and provides the first head-to-head evidence base for these five methods, supporting future work on broader domains, adaptive budgets/parameters, multi-objective and noisy/dynamic settings, and runtime–quality trade-offs.

Keywords

Ethical Statement

The authors declare that this study was conducted in accordance with internationally accepted principles of research and publication ethics. The manuscript is original, has not been published previously, and is not under consideration for publication elsewhere. All sources have been appropriately cited, and no ethical approval was required as the study does not involve human participants, animals, or sensitive data.

Thanks

The authors would like to thank Sivas Cumhuriyet University for providing the computational resources and research environment that supported this study. No external funding was received for this research.

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Details

Primary Language

English

Subjects

Artificial Intelligence (Other)

Journal Section

Research Article

Authors

Fatih Demirel ^*
0009-0006-0106-6433
Türkiye

Sibel Arslan
0000-0003-3626-553X
Türkiye

Publication Date

December 31, 2025

Submission Date

June 26, 2025

Acceptance Date

October 10, 2025

Published in Issue

Year 2025 Volume: 14 Number: 4

DOI

https://doi.org/10.17798/bitlisfen.1728348

IZ

https://izlik.org/JA93HD95KY

Cite

RIS / Bibtex

APA

Demirel, F., & Arslan, S. (2025). A Comprehensive Benchmark Study on Five Novel Metaheuristics: Flood, Football Training, Goat, Human Evolutionary, and Tornado-Based Optimization Approaches. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, 14(4), 2263-2284. https://doi.org/10.17798/bitlisfen.1728348

AMA

1.Demirel F, Arslan S. A Comprehensive Benchmark Study on Five Novel Metaheuristics: Flood, Football Training, Goat, Human Evolutionary, and Tornado-Based Optimization Approaches. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi. 2025;14(4):2263-2284. doi:10.17798/bitlisfen.1728348

Chicago

Demirel, Fatih, and Sibel Arslan. 2025. “A Comprehensive Benchmark Study on Five Novel Metaheuristics: Flood, Football Training, Goat, Human Evolutionary, and Tornado-Based Optimization Approaches”. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi 14 (4): 2263-84. https://doi.org/10.17798/bitlisfen.1728348.

EndNote

Demirel F, Arslan S (December 1, 2025) A Comprehensive Benchmark Study on Five Novel Metaheuristics: Flood, Football Training, Goat, Human Evolutionary, and Tornado-Based Optimization Approaches. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi 14 4 2263–2284.

IEEE

[1]F. Demirel and S. Arslan, “A Comprehensive Benchmark Study on Five Novel Metaheuristics: Flood, Football Training, Goat, Human Evolutionary, and Tornado-Based Optimization Approaches”, Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, vol. 14, no. 4, pp. 2263–2284, Dec. 2025, doi: 10.17798/bitlisfen.1728348.

ISNAD

Demirel, Fatih - Arslan, Sibel. “A Comprehensive Benchmark Study on Five Novel Metaheuristics: Flood, Football Training, Goat, Human Evolutionary, and Tornado-Based Optimization Approaches”. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi 14/4 (December 1, 2025): 2263-2284. https://doi.org/10.17798/bitlisfen.1728348.

JAMA

1.Demirel F, Arslan S. A Comprehensive Benchmark Study on Five Novel Metaheuristics: Flood, Football Training, Goat, Human Evolutionary, and Tornado-Based Optimization Approaches. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi. 2025;14:2263–2284.

MLA

Demirel, Fatih, and Sibel Arslan. “A Comprehensive Benchmark Study on Five Novel Metaheuristics: Flood, Football Training, Goat, Human Evolutionary, and Tornado-Based Optimization Approaches”. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, vol. 14, no. 4, Dec. 2025, pp. 2263-84, doi:10.17798/bitlisfen.1728348.

Vancouver

1.Fatih Demirel, Sibel Arslan. A Comprehensive Benchmark Study on Five Novel Metaheuristics: Flood, Football Training, Goat, Human Evolutionary, and Tornado-Based Optimization Approaches. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi. 2025 Dec. 1;14(4):2263-84. doi:10.17798/bitlisfen.1728348