Grey Wolf Optimization (GWO) algorithm is a new meta-heuristic method, which is inspired
by grey wolves, to mimic the hierarchy of leadership and grey wolves hunting mechanism in …

This paper proposed a solution to improve the grey wolf optimizer performance with
integrate the invasion-based migration operation. The traditional grey wolf optimizer …

Classification of objects has been a significant area of concern in machine vision
applications. In recent years, Support Vector Machines (SVM) is gaining popularity as an …

Bio-inspired computation is one of the emerging soft computing techniques of the past
decade. Although they do not guarantee optimality, the underlying reasons that make such …

In this work, a novel binary version of the grey wolf optimization (GWO) is proposed and
used to select optimal feature subset for classification purposes. Grey wolf optimizer (GWO) …

Classification is one of the main areas of study today, due to increased emphasis on
developing technologies that resemble human behavior. With advancements in the study of …

The proposed algorithm presents a solution to improve the grey wolf optimizer performance
using weighted distance and immigration operation. The weight distance is used for the …

Among various feature selection methods proposed in literature, meta-heuristic algorithms
as a wrapper-based method are widely used in feature subset selection problems. The …

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

Abstract Grey Wolf Optimizer (GWO) algorithm is a relatively new algorithm in the field of
swarm intelligence for solving continuous optimization problems as well as real world …