Sand cat swarm optimization: A comprehensive review of algorithmic advances, structural enhancements, and engineering applications

Metaheuristic algorithms, as powerful computational tools, play a significant role in solving complex optimization problems in the field of engineering. Among these algorithms, the Sand Cat Swarm Optimization (SCSO) algorithm, inspired by the hunting behaviour of sand cats, has shown considerable potential in addressing combinatorial problems and real-world applications. In this survey paper, a systematic and comprehensive review of the basic structure and extended versions of the SCSO has been conducted. Papers related to SCSO have been collected from 5 major databases (Elsevier, Springer, IEEE, MDPI, and Wiley). Elsevier and Springer contain the largest share of articles, with 32% and 26%, respectively. In this paper, binary, multi-objective, and hybrid versions have been thoroughly reviewed. Also, the application of the SCSO in various engineering fields, including structural engineering, energy systems, biomedical computing, and control systems, has been fully investigated. The field of engineering problems and Electronics-Power include the highest percentage of SCSO usage, with 20% and 24%, respectively. The results of statistical analyses show that the improved versions of SCSO outperform the basic metaheuristic algorithms in stability of results, convergence speed, and final quality of answers.