Despite Grey Wolf Optimizer's (GWO) superior performance in many areas, stagnation in
local optima areas may still be a concern. Several significant GWO factors can be explored …

A binary version of the hybrid grey wolf optimization (GWO) and particle swarm optimization
(PSO) is proposed to solve feature selection problems in this paper. The original PSOGWO …

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 …

Grey Wolf Optimizer (GWO) simulates the grey wolves' nature in leadership and hunting
manners. GWO showed a good performance in the literature as a meta-heuristic algorithm …

Salp swarm algorithm (SSA) is a recently developed meta-heuristic swarm intelligence
optimization algorithm based on simulating the chain movement behavior of salps sailing …

The Arithmetic Optimization Algorithm (AOA) is a recently proposed metaheuristic algorithm
that has been shown to perform well in several benchmark tests. The AOA is a metaheuristic …

Introduction: In pattern recognition and data mining, feature selection is one of the most
crucial tasks. To increase the efficacy of classification algorithms, it is necessary to identify …

Feature selection is an important data processing method to reduce dimension of the raw
datasets while preserving the information as much as possible. In this paper, an enhanced …

Grey wolf optimizer (GWO) is a very efficient metaheuristic inspired by the hierarchy of the
Canis lupus wolves. It has been extensively employed to a variety of practical applications …

Feature selection or dimensionality reduction can be considered as a multi-objective
minimization problem with two objectives: minimizing the number of features and minimizing …