Moth search algorithm: a bio-inspired metaheuristic algorithm for global optimization problems
By Gaige Wang. Memetic Computing, 2016
732 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Cellular and Molecular Neuroscience, Computational Theory and Mathematics
View Paper: https://www.semanticscholar.org/paper/c111c766afb9c83cad5af61ea8ea6217d485fac6
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Tong Han
- Zhuoran Zhang
- Ahmet Cosar
- Kusum Deep
- Tansel Dökeroğlu
- Swagatam Das
- Tayfun Kucukyilmaz
- Andi Tang
In Cellular and Molecular Neuroscience
In Computational Theory and Mathematics
- Tong Han
- Zhuoran Zhang
- Ahmet Cosar
- Kusum Deep
- Tansel Dökeroğlu
- Swagatam Das
- Tayfun Kucukyilmaz
- Andi Tang
Citations in the corpus
- A survey on new generation metaheuristic algorithms (2019, background): “Wang (2018) proposes Moth Search algorithm that depends on the characteristics of moths that have been the propensity to follow Lvy flights.”
- Tuna Swarm Optimization: A Novel Swarm-Based Metaheuristic Algorithm for Global Optimization (2021, methodology): “Some other algorithms of this category include: grey wolf optimizer (GWO) [30], monarch butterfly optimization (MBO) [31], elephant herding optimization (EHO) [32], moth search algorithm (MSA) [33], manta ray foraging optimization (MRFO) [34],earthworm optimization algorithm (EOA) [35], etc.”
- An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges (2023, methodology): “Such few techniques have been described by Wang and Tan (2017). Numerous improved methods have been developed to get better results in comparison”