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<p>Recently, the Snake Optimizer (SO) made great strides to realize new improvements, which brought another metaheuristic technique inspired by the snakes in nature because of their social and behavioral activity. SO is built on a very simple, understandable design with ways of exploration, feeding, and mating that help in driving the searches toward yielding superior quality solutions. The SO algorithm is suitable for solving many different kinds of optimization problems and issues efficiently, both constrained and unconstrained. SO simulates and investigates the temperature-related behaviors of both the individual snake members of a social structure and their interaction with each other. Thus, SO can apply adaptive control approaches throughout the various stages of optimization, enabling the system to maintain a balance between global and local search processes. Additionally, SO has many advantages, such as being derivative-free, easy to implement, having a simple parameter structure, and performing competitively with other algorithms, leading to an increasing number of applications for SO in computational and engineering optimization fields. This survey reviews and discusses the source studies underlying the SO. It follows the development of the algorithm and identifies the significant contributions of the literature that proved its efficiency and applicability for different optimization tasks. The work surveys the enhancements, variants, and hybrid extensions that have been proposed for improving the behavioral mechanisms of the algorithm, with an accent on increasing the exploration–exploitation balance and adapting the search process to real-world problem landscapes. It also includes a structured overview of SO solvers and implementations, with a detailed description of their organization in view of practical use within various optimization contexts. After that, a critical assessment of convergence characteristics and inherent limitations of the algorithm is carried out with the aim of identifying unresolved challenges. Finally, the work summarizes the main findings and outlines promising avenues of research with a view to further refinement and wider adoption of the SO algorithm.</p>

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A Comprehensive Review of the Snake Optimizer: Advancements, Variants, and Applications

  • 期刊论文

  • 发表日期：

    2026年6月16日

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  • Nora El-Rashidy,
  • Rahma Ezzat,
  • Abdelmawla Yousef

摘要

Recently, the Snake Optimizer (SO) made great strides to realize new improvements, which brought another metaheuristic technique inspired by the snakes in nature because of their social and behavioral activity. SO is built on a very simple, understandable design with ways of exploration, feeding, and mating that help in driving the searches toward yielding superior quality solutions. The SO algorithm is suitable for solving many different kinds of optimization problems and issues efficiently, both constrained and unconstrained. SO simulates and investigates the temperature-related behaviors of both the individual snake members of a social structure and their interaction with each other. Thus, SO can apply adaptive control approaches throughout the various stages of optimization, enabling the system to maintain a balance between global and local search processes. Additionally, SO has many advantages, such as being derivative-free, easy to implement, having a simple parameter structure, and performing competitively with other algorithms, leading to an increasing number of applications for SO in computational and engineering optimization fields. This survey reviews and discusses the source studies underlying the SO. It follows the development of the algorithm and identifies the significant contributions of the literature that proved its efficiency and applicability for different optimization tasks. The work surveys the enhancements, variants, and hybrid extensions that have been proposed for improving the behavioral mechanisms of the algorithm, with an accent on increasing the exploration–exploitation balance and adapting the search process to real-world problem landscapes. It also includes a structured overview of SO solvers and implementations, with a detailed description of their organization in view of practical use within various optimization contexts. After that, a critical assessment of convergence characteristics and inherent limitations of the algorithm is carried out with the aim of identifying unresolved challenges. Finally, the work summarizes the main findings and outlines promising avenues of research with a view to further refinement and wider adoption of the SO algorithm.