.png){kind=icon}
.png){kind=icon}
Return
Grey wolf optimizer and whale optimization algorithm: a systematic review
.png){kind=icon}
OA
AI
DOI：10.1007/s10462-026-11585-8.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Metaheuristic algorithms that take inspiration from nature’s processes are frequently utilized for solving very complicated optimization problems due to their flexibility and efficiency. Within the context of this, the grey wolf optimizer (GWO) and the whale optimization algorithm (WOA) have become two of the most popular metaheuristics since they have a relatively simple structure along with good performance characteristics. However, both algorithms suffer from similar limitations, such as premature convergence and not having a good balance between their ability to explore the search space and their ability to exploit good locations in the search space. These limitations have led many researchers to develop new and enhanced versions of the GWO and WOA algorithms. Although there have been many papers published on these topics due to the exponential increase in research activity surrounding this area of study, there currently exists a gap between researchers who have published enhancements to these algorithms and researchers who would like to build upon or further develop enhancements to these algorithms. The purpose of this paper is to provide researchers with a complete and structured overview of the most recent advances in GWO and WOA. Thus, we utilized five academic databases (ScienceDirect, SpringerLink, IEEE Xplore, and ACM Digital Library) for gathering different researches and a modified version of the PRISMA method has been used for the identification, screening, and analysis of recent papers that contain improvements to the GWO or WOA algorithms. We categorize the identified papers based on their improvement methodology, hybridization strategy, and application area. Finally, we identify some current research opportunities and describe a few high-priority areas of future research activities.
Keywords:
Swarm intelligence
Optimization
Metaheuristics
Grey wolf optimizer
Whale optimization algorithm
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
13.9
Papers:
6.1K
Citations:
1.9W