A Systematic Literature Review on the Sand Cat Swarm Algorithm: Enhancements, Applications, and Future Directions

• Published in: International journal of advanced computer science & applications Vol. 16; no. 3
• Main Authors: Ahmad, Wirawati Dewi, Bakar, Azuraliza Abu, Khalid, Mohd Nor Akmal
• Format: Journal Article
• Language: English
• Published: West Yorkshire Science and Information (SAI) Organization Limited 2025
• Subjects: Algorithms; Design engineering; Design optimization; Heuristic methods; Job shop scheduling; Literature reviews; Machine learning; Optimization techniques; Systematic review
• ISSN: 2158-107X; 2156-5570; 2156-5570
• DOI: 10.14569/IJACSA.2025.0160354

The Sand Cat Swarm Algorithm (SCSA) has emerged as a promising metaheuristic optimization technique inspired by the behavior of sand cats in their natural habitat. This paper presents a systematic literature review synthesizes the enhancement, performance comparing algorithms, applications of SCSA across various domains and future direction on SCSA enhancement. The study aims to contribute to the evolution, enhancements, applications, and performance of the Sand Cat Swarm Algorithm (SCSA), providing a comprehensive analysis of its development, performances evaluation, application, limitations, and future research opportunities in SCSA in solving optimization problems. The SLR methodology was applied, and a total of 77 scientific articles were analyzed. The analysis reveals that SCSA demonstrates competitive performance across a wide range of benchmark problems and real-world applications in engineering, computer science, and other fields such as engineering design optimization, feature selection, energy systems optimization, flexible job shop scheduling and medical diagnosis problems. This review also identifies several key strengths of SCSA, including its ability to balance exploration and exploitation effectively, its adaptability to various problem domains, and its potential for hybridization with other algorithms. Lastly, this paper outlines potential improvements and future research directions, such as the development of multi-objective SCSA variants, integration with machine learning techniques, and exploration of parallel and distributed implementations. Overall, this paper provides researchers and practitioners with valuable insights into the current state of SCSA, its practical applications, and promising avenues for future research in the field of metaheuristic optimization.