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<p>The Mountain Gazelle Optimizer (MGO) is a newly emerging nature-inspired metaheuristic algorithm based on mountain gazelles' regionally and adaptively directed behavior. It is intended to solve complex optimization problems with an effective balance of exploration and exploitation. The MGO has several benefits: it is scalable, adaptable, parameter-free,&#xa0;capable of multi-objective optimization , and offers real-world application opportunities. The drawbacks of MGO include susceptibility to premature convergence, high computational complexity, and limited scalability to solve higher dimensional&#xa0;problems. The focus of the work is to investigate the development of MGO in the optimization field thoroughly. This review addresses the capabilities and limitations and express its growing relevance across applications. The investigation will refer to 89 studies published on MGO, categorized into four headings: adapted, variants, hybrid, and enhanced, contributing 37, 3, 33, and 27%, respectively, of all studies. This review is to supply researchers and practitioners with a comprehensive overview of potential optimization strategies. The review will compile and synthesize relevant studies to portray potential development opportunities for MGO and practical applications.</p>

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Advances in Mountain Gazelle Optimizer: A Comprehensive Study on its Classification and Applications

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月6日

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  • Ferzat Anka,
  • Farhad Soleimanian Gharehchopogh,
  • Ghanshyam G. Tejani,
  • Seyed Jalaleddin Mousavirad

摘要

The Mountain Gazelle Optimizer (MGO) is a newly emerging nature-inspired metaheuristic algorithm based on mountain gazelles' regionally and adaptively directed behavior. It is intended to solve complex optimization problems with an effective balance of exploration and exploitation. The MGO has several benefits: it is scalable, adaptable, parameter-free, capable of multi-objective optimization , and offers real-world application opportunities. The drawbacks of MGO include susceptibility to premature convergence, high computational complexity, and limited scalability to solve higher dimensional problems. The focus of the work is to investigate the development of MGO in the optimization field thoroughly. This review addresses the capabilities and limitations and express its growing relevance across applications. The investigation will refer to 89 studies published on MGO, categorized into four headings: adapted, variants, hybrid, and enhanced, contributing 37, 3, 33, and 27%, respectively, of all studies. This review is to supply researchers and practitioners with a comprehensive overview of potential optimization strategies. The review will compile and synthesize relevant studies to portray potential development opportunities for MGO and practical applications.