Citation :

Ab Wahid Bhat, Abhiruchi Passi, "Sand Cat Swarm Optimization: Implementation, Issues, and Existing Approaches," International Journal of Computer Science and Engineering, vol. 13, no. 4, pp. 51-55, 2026. Crossref, https://doi.org/10.14445/23488387/IJCSE-V13I4P104

Abstract

Population-based swarm-intelligence algorithms are metaheuristic algorithms inspired by the behaviour of animals, birds, and insects found in nature. These algorithms are based on a balanced strategy of exploration and exploitation and can be used to optimize multiple NP-hard problems. The Sand Cat Swarm Optimization (SCSO) algorithm is a novel and recently proposed algorithm based on the hunting behaviour of sand cats. These sand cats have extraordinarily low-frequency hearing that helps them locate and catch their prey. The sand cat swarm optimization algorithm has been successfully tested for solving various optimization problems, and it performed efficiently in comparison to other existing algorithms. However, the SCSO has certain limitations when it comes to convergence and optimality of the global solution. The main aim of the paper is to present a detailed description of the implementation of SCSO and highlight its limitations. In addition to this, various approaches proposed to enhance the performance of SCSO are mentioned, and the adopted methodology is analysed. The changes incorporated in the modified versions of SCSO have been tabulated at the end of the paper.

Keywords

Metaheuristic Algorithms, Sand Cat Swarm Optimization, Limitations, Approaches, Genetic Algorithm.

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