A comprehensive review on water cycle algorithm and its applications
- Authors
- 최영환; Mohammad Nasir; Ali Sadollah; 김중훈
- Issue Date
- Dec-2020
- Publisher
- Springer Verlag
- Citation
- Neural Computing and Applications, no.32, pp 1 - 56
- Pages
- 56
- Indexed
- SCIE
SCOPUS
- Journal Title
- Neural Computing and Applications
- Number
- 32
- Start Page
- 1
- End Page
- 56
- URI
- https://scholarworks.gnu.ac.kr/handle/sw.gnu/5824
- ISSN
- 0941-0643
1433-3058
- Abstract
- In recent years, significant attentions have been devoted to design of metaheuristic optimization algorithms in order to solve optimization problems. Metaheuristic optimizers are methods which are inspired by observing the phenomena occurring in nature. In this paper, a comprehensive and exhaustive review has been carried out on water cycle algorithm (WCA) and its applications in a wide variety of study fields. The WCA is one of the novel metaheuristic optimization algorithms which is inspired by water cycle process in nature and how streams and rivers flow into the sea. Good exploitation and exploration capabilities have made the WCA a good alternative for solving large-scale optimization problems. Due to its capabilities and strengths, the WCA has been utilized in many and various majors including mechanical engineering, electrical and electronic engineering, civil engineering, industrial engineering, water resources and hydropower engineering, computer engineering, mathematics, and so forth. A variety of articles based on WCA have been published in different international journals such as Elsevier, Springer, IEEE Transactions, Wiley, Taylor & Francis, and in the proceedings of international conferences as well, since 2012 to the present. Thus, it is highly believed that this paper can be appropriate, beneficial and practical for students, academic researchers, professionals, and engineers. Also, it can be an innovative and comprehensive reference for subsequent academic papers and books relevant to the WCA, optimization methods, and metaheuristic optimization algorithms.
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