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

IF 12.7 1区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Computer Science Review Pub Date : 2025-08-11 DOI:10.1016/j.cosrev.2025.100805

Mehdi Hosseinzadeh , Jawad Tanveer , Amir Masoud Rahmani , Farhad Soleimanian Gharehchopogh , Ramin Abbaszadi , Sang-Woong Lee , Jan Lansky

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引用次数: 0

Abstract

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.

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沙猫群优化：对算法进步、结构改进和工程应用的全面回顾

元启发式算法作为一种强大的计算工具，在解决工程领域的复杂优化问题中发挥着重要作用。在这些算法中，受沙猫捕食行为启发的沙猫群优化（SCSO）算法在解决组合问题和实际应用方面显示出相当大的潜力。在这篇调查报告中，对SCSO的基本结构和扩展版本进行了系统和全面的回顾。SCSO相关论文收录于5大数据库（Elsevier， b施普林格，IEEE， MDPI, Wiley）。爱思唯尔和b施普林格的文章占比最大，分别为32%和26%。本文对二元版本、多目标版本和混合版本进行了全面的综述。此外，SCSO在各种工程领域的应用，包括结构工程、能源系统、生物医学计算和控制系统，已经得到了充分的研究。工程问题和电子电力领域的SCSO使用比例最高，分别为20%和24%。统计分析结果表明，改进版本的SCSO在结果稳定性、收敛速度和最终答案质量方面优于基本的元启发式算法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Computer Science Review Computer Science-General Computer Science

CiteScore

32.70

自引率

0.00%

发文量

审稿时长

51 days

期刊介绍： Computer Science Review, a publication dedicated to research surveys and expository overviews of open problems in computer science, targets a broad audience within the field seeking comprehensive insights into the latest developments. The journal welcomes articles from various fields as long as their content impacts the advancement of computer science. In particular, articles that review the application of well-known Computer Science methods to other areas are in scope only if these articles advance the fundamental understanding of those methods.