A Comprehensive Survey on Snake Optimizer and Its Performance Evaluation in Image Clustering Field

Rebika Rai^1,*, Totan Bharasa², Arunita Das³, Krishna Gopal Dhal³
1 Department of Computer Applications, Sikkim University, Sikkim, India
2 Natural Science Research Centre of Belda College Affiliated to Vidyasagar University, Belda College, Paschim Medinipur, West Bengal, India
3 Department of Computer Science, Midnapore College (Autonomous), Paschim Medinipur, West Bengal, India
* Corresponding Author: Rebika Rai. Email: email

Computer Modeling in Engineering & Sciences https://doi.org/10.32604/cmes.2026.079037

Received 13 January 2026; Accepted 08 March 2026; Published online 07 April 2026

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Abstract

Snake Optimizer (SO) is a popular optimization algorithm developed by Hashim and Hussien, based on the competitive and selective mating nature of snakes. By emulating such natural methods, SO presents an intelligent method to solve complicated optimization problems, making it a valuable tool in various scientific and technological applications. This paper provides an extensive review of the SO, its inception, the development of different variants, and applications. This paper identifies several SO variants, such as improved SO variants using different strategies, hybridized SO variants with other metaheuristics, Binary SO variants to solve discrete optimization problems, and multi-objective SO variants to tackle many objectives. Furthermore, the applications of variants of SO demonstrate its adaptability across diverse fields. In addition, the paper discusses a few of the possible future research directions for SO. The performance of the SO has been evaluated in the clustering-based image segmentation domain and compared to other MAs. The numerical and statistical results clearly demonstrate the superiority of the SO to other tested MAs. With researchers engaging MA as an alternate methodology in solving almost every optimization challenge, this survey would definitely provide valuable perceptions to numerous researchers seeking to attain a thorough understanding of SO, its advancements, and its broad applications in resolving diverse optimization problems.