Awadallah Mohammed A, Al-Betar Mohammed Azmi, Doush Iyad Abu, Makhadmeh Sharif Naser, Al-Naymat Ghazi
Department of Computer Science, Al-Aqsa University, P.O. Box 4051, Gaza, Palestine.
Artificial Intelligence Research Center (AIRC), Ajman University, Ajman, United Arab Emirates.
Arch Comput Methods Eng. 2023;30(5):2831-2858. doi: 10.1007/s11831-023-09887-z. Epub 2023 Feb 7.
This paper reviews the latest versions and applications of sparrow search algorithm (SSA). It is a recent swarm-based algorithm proposed in 2020 rapidly grew due to its simple and optimistic features. SSA is inspired by the sparrow living style of foraging and the anti-predation behavior of sparrows. Since its establishment, it has been utilized for a plethora of optimization problems in different research topics, such as mechanical engineering, electrical engineering, civil engineering, power systems, industrial engineering, image processing, networking, environment, robotics, planing and scheduling, and healthcare. Initially, the growth of SSA and its theoretical features are highlighted in terms of the number of published articles, citations, topics covered, etc. After that, the different extended versions of SSA are reviewed, where the main variations of SSA are produced to avoid premature convergence and to boost the diversity aspects. These extended versions are modifications and hybridization summarized with more focus on the motivations behind establishing these versions. Multi-objective SSA is also presented as another version to deal with Multi-objective optimization problems. The critical analysis of the main research gaps in the convergence behaviour of SSA is discussed. Finally, the conclusion and the possible future expansions are recommended based on the research works accomplished in the literature.
本文综述了麻雀搜索算法(SSA)的最新版本及其应用。它是2020年提出的一种基于群体的新算法,因其简单和优良的特性而迅速发展。SSA的灵感来源于麻雀的觅食生活方式和反捕食行为。自其诞生以来,它已被应用于不同研究领域的大量优化问题,如机械工程、电气工程、土木工程、电力系统、工业工程、图像处理、网络、环境、机器人技术、规划与调度以及医疗保健等。首先,从发表文章的数量、引用次数、涵盖的主题等方面突出了SSA的发展及其理论特性。之后,对SSA的不同扩展版本进行了综述,其中产生了SSA的主要变体以避免过早收敛并提高多样性。这些扩展版本是修改和混合,更多地关注建立这些版本背后的动机。多目标SSA也作为处理多目标优化问题的另一个版本被提出。讨论了对SSA收敛行为中主要研究空白的批判性分析。最后,根据文献中完成的研究工作给出了结论和未来可能的扩展方向。