An Improved Binary Grey-Wolf Optimizer With Simulated Annealing for Feature Selection

Abstract

This paper proposes improvements to the binary grey-wolf optimizer (BGWO) to solve the feature selection (FS) problem associated with high data dimensionality, irrelevant, noisy, and redundant data that will then allow machine learning algorithms to attain better classification/clustering accuracy in less training time. We propose three variants of BGWO in addition to the standard variant, applying different transfer functions to tackle the FS problem. Because BGWO generates continuous values and FS needs discrete values, a number of V-shaped, S-shaped, and U-shaped transfer functions were investigated for incorporation with BGWO to convert their continuous values to binary. After investigation, we note that the performance of BGWO is affected by the selection of the transfer function. Then, in the first variant, we look to reduce the local minima problem by integrating an exploration capability to update the position of the grey wolf randomly within the search space with a certain probability; this variant was abbreviated as IBGWO. Consequently, a novel mutation strategy is proposed to select a number of the worst grey wolves in the population which are updated toward the best solution and randomly within the search space based on a certain probability to determine if the update is either toward the best or randomly. The number of the worst grey wolf selected by this strategy is linearly increased with the iteration. Finally, this strategy is combined with IBGWO to produce the second variant of BGWO that was abbreviated as LIBGWO. In the last variant, simulated annealing (SA) was integrated with LIBGWO to search around the best-so-far solution at the end of each iteration in order to identify better solutions. The performance of the proposed variants was validated on 32 datasets taken from the UCI repository and compared with six wrapper feature selection methods. The experiments show the superiority of the proposed improved variants in producing better classification accuracy than the other selected wrapper feature selection algorithms.INDEX TERMS Grey-wolf optimizer, feature selection, simulated annealing, mutation strategy.

High-Dimensional Feature Selection Based on Improved Binary Ant Colony Optimization Combined with Hybrid Rice Optimization Algorithm

“…In the work of Hu et al [34], an enhanced variant of the binary GWO was introduced, incorporating a novel strategy for updating the parameter governing exploration and exploitation, along with fve transfer functions for mapping continuous values to their binary counterparts, thereby enhancing the quality of candidate solutions. Likewise, Abdel-Basset et al [35] proposed three distinct binary GWO variants, each utilizing diferent transfer functions. In addition to GWO-based research, PSO has been a focus in prior studies.…”

EBBA: An Enhanced Binary Bat Algorithm Integrated with Chaos Theory and Lévy Flight for Feature Selection

“…Ouadfel et al [31] proposed a hybrid feature selection approach based on the ReliefF filter method and equilibrium optimizer (EO), which is composed of two phases and tested in some open datasets. Abdel-Basset et al [14] proposed three variants of BGWO in addition to the standard variant, applying different transfer functions to tackle the feature selection problem. In [32], two different wrapper feature selection approaches were proposed based on farmland fertility algorithm (FFA), which denoted as BFFAS and BFFAG, and these methods are effective in solving feature selection problems.…”

“…Thus, it can be seen as an effective method to overcome the challenges of feature selection. For instance, some well-known swarm intelligence, i.e., genetic algorithm (GA) [10], particle swarm optimization (PSO) [11], dragonfly algorithm (DA) [12], ant-lion optimizer (ALO) [13], and grey wolf optimizer (GWO) [14] has been applied in feature selection.…”

Binary modified Cat and Mouse based Optimizer for medical feature selection: A COVID-19 case study

“…The FB methods classify features based on their inherent characteristics, e.g., dependency, information consistency, rank, etc., without using ML methods [27]. Although FB methods are faster and have a lower computational cost than WB methods because they do not call ML tools, they typically cannot offer satisfactory solutions [43,44]. This is because the selected features do not significantly affect the performance of ML methods [1,8,45] .…”