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

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...

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Bibliographic Details
Published in:International journal of computational intelligence systems Vol. 18; no. 1; pp. 247 - 49
Main Authors: Anka, Ferzat, Gharehchopogh, Farhad Soleimanian, Tejani, Ghanshyam G., Mousavirad, Seyed Jalaleddin
Format: Journal Article
Language:English
Published: Dordrecht Springer Netherlands 06.10.2025
Springer Nature B.V
Springer
Subjects:
Adjustment
Artificial Intelligence
Classification
Complexity
Computational Intelligence
Control
Engineering
Exploitation
Genetic algorithms
Heuristic methods
Hybrid methods
Hybrid methods
Improved performance
Improved performance
Mathematical Logic and Foundations
Mechatronics
Metaheuristic algorithm
Mountain gazelle optimizer
Mountains
Multiple objective analysis
Mutation
Optimization
Optimization algorithms
Real time
Robotics
Improved performance
Metaheuristic algorithm
Hybrid methods
Mountain gazelle optimizer
Classification
ISSN:1875-6883, 1875-6891, 1875-6883
Online Access:Get full text
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Summary: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.
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ISSN:1875-6883
1875-6891
1875-6883
DOI:10.1007/s44196-025-00968-4