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Journals / Sakarya University Journal of Science / 2026 / Volume: 28 Issue: 3

A Detailed Comparison of Two New Heuristic Algorithms Based on Gazelles Behavior

Emine Baş(KONYA TEKNİK ÜNİVERSİTESİ)0000-0003-4322-6010
Pages
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Publication Date
2024-06-30
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

According to the results, MGO is more successful than GOA in all dimensions.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In this study, Mountain Gazelle Optimization (MGO) and Gazelle Optimization Algorithm (GOA) algorithms, which have been newly proposed in recent years, were examined.
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Key Findings & Data:This study was conducted to resolve this confusion and demonstrate the discovery and exploitation success of both algorithms.
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Conclusion & Impact:According to the results, MGO is more successful than GOA in all dimensions.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Gazelle · Exploration · Exploitation

Abstract

In this study, Mountain Gazelle Optimization (MGO) and Gazelle Optimization Algorithm (GOA) algorithms, which have been newly proposed in recent years, were examined. Although MGO and GOA are different heuristic algorithms, they are often considered the same algorithms by researchers. This study was conducted to resolve this confusion and demonstrate the discovery and exploitation success of both algorithms. While MGO developed the exploration and exploitation ability by being inspired by the behavior of gazelles living in different groups, GOA model was developed by being inspired by the behavior of gazelles in escaping from predators, reaching safe environments and grazing in safe environments. MGO and GOA were tested on 13 classical benchmark functions in seven different dimensions and their success was compared. According to the results, MGO is more successful than GOA in all dimensions. GOA, on the other hand, works faster than MGO. Additionally, MGO and GOA were tested on three different engineering design problems. While MGO was more successful in the tension/compression spring design problem and welded beam design problems, GOA achieved better results in the pressure vessel design problem. The results show that MGO improves the ability to explore and avoid local traps better than GOA. MGO and GOA are also compared with three different heuristic algorithms selected from the literature (GSO, COA, and ZOA). According to the results, MGO has shown that it can compete with new algorithms in the literature. GOA, on the other hand, lags behind comparison algorithms.

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Cite This Article

Baş, E. (2024). A Detailed Comparison of Two New Heuristic Algorithms Based on Gazelles Behavior. Sakarya University Journal of Science, Volume: 28 Issue: 3. https://doi.org/10.16984/saufenbilder.1399655

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References (23)

  1. L. Abualigah, D. Yousri, M. Abd Elaziz, A. A. Ewees, M. A. Al-Qaness, A. H. Gandomi, “Aquila optimizer: a novel meta-heuristic optimization algorithm,” Computers & Industrial Engineering, vol. 157, pp. 1-37, 2021.
  2. C. L. Hwang, A. S. M. Masud, “Multiple Objective Decision Making— Methods and Applications: A State-of-the-Art Survey,” Berlin, Germany, Springer, 2012.
  3. P. Agrawal, H. F. Abutarboush, T. Ganesh, A. W. Mohamed, ‘‘Metaheuristic algorithms on feature selection: A survey of one decade of research (2009–2019),’’ IEEE Access, vol. 9, pp. 26766–26791, 2021.
  4. B. Abdollahzadeh, F. S. Gharehchopogh, N. Khodadadi, S. Mirjalili, “Mountain gazelle optimizer: A new nature-inspired metaheuristic algorithm for global optimization problems,” Advances in Engineering Software, vol. 174, no. 103282, 2022.
  5. J. O. Agushaka, A. E. Ezugwu, L. Abualigah, “Gazelle optimization algorithm: a novel nature-inspired metaheuristic optimizer," Neural Computing and Applications, vol. 35(5), pp. 4099-4131, 2023.
  6. A. A., Heidari, S. Mirjalili, H. Faris, I. Aljarah, M. Mafarja, H. Chen, “Harris hawks optimization: Algorithm and applications,” Future Generation Computer Systems, vol. 97, pp. 849–872, 2019.
  7. S. Li, H. Chen, M. Wang, A. A. Heidari, S. Mirjalili, “Slime mould algorithm: A new method for stochastic optimization,” Future Generation Computer Systems, vol. 111, pp. 300–323, 2020.
  8. S. Kaur, L. K. Awasthi, A. L. Sangal, G. Dhiman, “Tunicate swarm algorithm: A new bio-inspired based metaheuristic paradigm for global optimization,” Engineering Applications of Artificial Intelligence, vol. 90, pp. 103541, 2020.
  9. A. Faramarzi, M. Heidarinejad, B. Stephens, S. Mirjalili, “Equilibrium optimizer: A novel optimization algorithm,” Knowledge-Based Systems, Vol. 191, pp. 105190, 2020.
  10. L. Abualigah, A. Diabat, S. Mirjalili, M. A. Elaziz, A. H. Gandomi, “The arithmetic optimization algorithm,” Computer Methods Applied Mechanics and Engineering, vol. 376, pp. 113609, 2021.
  11. A. Faramarzi, M. Heidarinejad, S. Mirjalili, A. H. Gandomi, “Marine predators algorithm: A nature-inspired metaheuristic,” Expert Systems with Applications, vol. 152, pp. 113377, 2020.
  12. E. Trojovská, M. Dehghani, P. Trojovský, “Zebra optimization algorithm: A new bio-inspired optimization algorithm for solving optimization algorithm,” IEEE Access, vol. 10, pp. 49445-49473, 2022.
  13. H. Jia, H. Rao, C. Wen, S. Mirjalili, “Crayfish optimization algorithm,” Artificial Intelligence Review, pp. 1-61, 2023.
  14. M. Noroozi, H. Mohammadi, E. Efatinasab, A. Lashgari, M. Eslami, B. Khan, “Golden search optimization algorithm,” IEEE Access, vol. 10, pp. 37515-37532, 2022.
  15. E. Baş, E. Ülker, “Comparison between SSA and SSO algorithm inspired in the behavior of the social spider for constrained optimization,” Artificial Intelligence Review, vol. 54(7), pp. 5583-5631, 2021.
  16. E. Baş, A. İhsan, “Performance analysis and comparison of gray wolf optimization and Krill herd optimization algorithm,” Pamukkale University Journal of Engineering Sciences, vol. 1000(1000), pp. 0-0, 2023.
  17. G. A. Grau, F. R. Walther, “Mountain gazelle agonistic behaviour,” Animal Behavior, vol. 24 (3), pp. 626–36, 1976.
  18. S. Omondi, “Gazelle Facts - Animals of the World. Retrieved from WorldAtlas / Environment, (2017, August 1). [Online]. Available: https://www. worldatlas.com/articles/gazelle-facts-animals-of-the-world.html.
  19. A. Einstein, “Investigations on the Theory of the Brownian Movement Courier Corporation,” US, 1956.
  20. R. N. Mantegna, “Fast, accurate algorithm for numerical simulation of Levy stable stochastic processes,” Physical Review E, vol. 49(5), pp. 4677, 1994.
  21. R. Rajabioun, “Cuckoo optimization algorithm,” Applied soft computing, vol. 11(8), pp. 5508-5518, 2011.
  22. A. Ahrari, M. R. Saadatmand, M. Shariat-Panahi, A. A. Atai, “On the limitations of classical benchmark functions for evaluating robustness of evolutionary algorithms,” Applied Mathematics and Computation, vol. 215(9), pp. 3222-3229, 2010.
  23. F. Wilcoxon, ‘‘Individual comparisons by ranking methods,’’ in Break throughs in Statistics, New York, NY, USA: Springer, 1992, pp. 196–202.