Improved Binary Grey Wolf Optimizer and Its application for feature selection
By Pei Hu, Jeng‐Shyang Pan, Shu-Chuan Chu. Knowledge-Based Systems, 2020
355 citations recorded by the source corpus.
Abstract Grey Wolf Optimizer (GWO) is a new swarm intelligence algorithm mimicking the behaviours of grey wolves. Its abilities include fast convergence, simplicity and easy realization. It has been proved its superior performance and widely used to optimize the continuous applications, such as, cluster analysis, engineering problem, training neural network and etc. However, there are still some binary problems to optimize in the real world. Since binary can only be taken from values of 0 or 1, the standard GWO is not suitable for the problems of discretization. Binary Grey Wolf Optimizer (BGWO) extends the application of the GWO algorithm and is applied to binary optimization issues. In the position updating equations of BGWO, the a parameter controls the values of A and D , and influences algorithmic exploration and exploitation. This paper analyses the range of values of A D under binary condition and proposes a new updating equation for the a parameter to balance the abilities of global search and local search. Transfer function is an important part of BGWO, which is essential for mapping the continuous value to binary one. This paper includes five transfer functions and focuses on improving their solution quality. Through verifying the benchmark functions, the advanced binary GWO is superior to the original BGWO in the optimality, time consumption and convergence speed. It successfully implements feature selection in the UCI datasets and acquires low classification errors with few features.
Subfields: Artificial Intelligence, Control and Systems Engineering
View Paper: https://www.semanticscholar.org/paper/e06e82b26f19bf31d76d5c5789d03140b83950a1
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
In Control and Systems Engineering
Citations in the corpus
- Metaheuristic Algorithms on Feature Selection: A Survey of One Decade of Research (2009-2019) (2021, neutral): “Hu et al. [176] proposed new transfer functions and new updating scheme for parameters of GWO.”
- A comprehensive survey on recent metaheuristics for feature selection (2022, background): “variant of GWO by mapping the transfer functions to binary representations [169].”