| Review of the grey wolf optimization algorithm: variants and applications | |
| Liu, Yunyun1,2; As'arry, Azizan2; Hassan, Mohd Khair2; Hairuddin, Abdul Aziz2; Mohamad, Hesham2 | |
| 2024-02 | |
| 发表期刊 | NEURAL COMPUTING & APPLICATIONS
 |
| ISSN | 0941-0643 |
| EISSN | 1433-3058 |
| 卷号 | 36期号:6页码:2713-2735 |
| 摘要 | One of the most widely referenced Swarm Intelligence (SI) algorithms is the Grey Wolf Optimizer (GWO), which is based on the pack hunting and natural leadership organization of grey wolves. The GWO algorithm offers several significant benefits, including simple implementation, rapid convergence, and superior convergence outcomes, leading to its effective application in diverse fields for solving optimization issues. Consequently, the GWO has rapidly garnered substantial research interest and a broad audience across numerous areas. To better understand the literature on this algorithm, this review paper aims to consolidate and summarize research publications that utilized the GWO. The paper begins with a concise introduction to the GWO, providing insight into its natural establishment and conceptual framework for optimization. It then lays out the theoretical foundation and key procedures involved in the GWO, following which it comprehensively examines the most recent iterations of the algorithm and categorizes them into parallel, modified, and hybridized variations. Subsequently, the primary applications of the GWO are thoroughly explored, spanning various fields such as computer science, engineering, energy, physics and astronomy, materials science, environmental science, and chemical engineering, among others. This review paper concludes by summarizing the key arguments in favour of GWO and outlining potential lines of inquiry in the future research. |
| 关键词 | GWO Swarm intelligence algorithms Variants Applications |
| DOI | 10.1007/s00521-023-09202-8 |
| 收录类别 | SCIE ; EI |
| 语种 | 英语 |
| WOS研究方向 | Computer Science |
| WOS类目 | Computer Science, Artificial Intelligence |
| WOS记录号 | WOS:001147954900001 |
| 出版者 | SPRINGER LONDON LTD |
| EI入藏号 | 20234715103823 |
| 原始文献类型 | Article |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | https://ir.cqcet.edu.cn/handle/39TD4454/18228 |
| 专题 | 重庆电子科技职业大学 |
| 作者单位 | 1.Chongqing Coll Elect Engn, Sch Intelligent Mfg & Automobile, Chongqing, Peoples R China; 2.Univ Putra Malaysia, Fac Engn, Seri Kembangan, Malaysia |
| 推荐引用方式 GB/T 7714 | Liu, Yunyun,As'arry, Azizan,Hassan, Mohd Khair,et al. Review of the grey wolf optimization algorithm: variants and applications[J]. NEURAL COMPUTING & APPLICATIONS,2024,36(6):2713-2735. |
| APA | Liu, Yunyun,As'arry, Azizan,Hassan, Mohd Khair,Hairuddin, Abdul Aziz,&Mohamad, Hesham.(2024).Review of the grey wolf optimization algorithm: variants and applications.NEURAL COMPUTING & APPLICATIONS,36(6),2713-2735. |
| MLA | Liu, Yunyun,et al."Review of the grey wolf optimization algorithm: variants and applications".NEURAL COMPUTING & APPLICATIONS 36.6(2024):2713-2735. |
| 条目包含的文件 | 条目无相关文件。 | |||||
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。
修改评论