Chaotic diffusion‐limited aggregation enhanced grey wolf optimizer: Insights, analysis, binarization, and feature selection

2021 ◽  
Author(s):  
Jiao Hu ◽  
Ali Asghar Heidari ◽  
Lejun Zhang ◽  
Xiao Xue ◽  
Wenyong Gui ◽  
...  
Keyword(s):  
Feature Selection ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Diffusion Limited Aggregation ◽  
Diffusion Limited ◽  
Chaotic Diffusion

A new fusion of grey wolf optimizer algorithm with a two-phase mutation for feature selection

2020 ◽  
Vol 139 ◽  
pp. 112824 ◽  
Author(s):  
Mohamed Abdel-Basset ◽  
Doaa El-Shahat ◽  
Ibrahim El-henawy ◽  
Victor Hugo C. de Albuquerque ◽  
Seyedali Mirjalili
Keyword(s):  
Feature Selection ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Two Phase ◽  
Phase Mutation

scholarly journals MbGWO-SFS: Modified Binary Grey Wolf Optimizer Based on Stochastic Fractal Search for Feature Selection

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 107635-107649 ◽  
Author(s):  
El-Sayed M. El-Kenawy ◽  
Marwa Metwally Eid ◽  
Mohamed Saber ◽  
Abdelhameed Ibrahim
Keyword(s):  
Feature Selection ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Stochastic Fractal Search

Unsupervised hyperspectral feature selection based on fuzzy c-means and grey wolf optimizer

2018 ◽  
Vol 40 (9) ◽  
pp. 3344-3367 ◽  
Author(s):  
Fuding Xie ◽  
Cunkuan Lei ◽  
Fangfei Li ◽  
Dan Huang ◽  
Jun Yang
Keyword(s):  
Feature Selection ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Fuzzy C Means

scholarly journals A Grey Wolf Optimizer Feature Selection method and its Effect on the Performance of Document Classification Problem

2021 ◽  
Vol 4 (2) ◽  
pp. 116-122
Author(s):  
Ibraheem Al-Jadir ◽  
Waleed A. Mahmoud
Keyword(s):  
Feature Selection ◽  
Feature Selection Method ◽  
Optimization Methods ◽  
Classification Problem ◽  
Performance Outcomes ◽  
Grey Wolf Optimizer ◽  
Great Success ◽  
Grey Wolf ◽  
Feature Selection Problem ◽  
Krill Herd

Optimization methods are considered as one of the highly developed areas in Artificial Intelligence (AI). The success of the Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) has encouraged researchers to develop other methods that can obtain better performance outcomes and to be more responding to the modern needs. The Grey Wolf Optimization (GWO), and the Krill Herd (KH) are some of those methods that showed a great success in different applications in the last few years. In this paper, we propose a comparative study of using different optimization methods including KH and GWO in order to solve the problem of document feature selection for the classification problem. These methods are used to model the feature selection problem as a typical optimization method. Due to the complexity and the non-linearity of this kind of problems, it becomes necessary to use some advanced techniques to make the judgement of which features subset that is optimal to enhance the performance of classification of text documents. The test results showed the superiority of GWO over the other counterparts using the specified evaluation measures.

Multi-strategy ensemble grey wolf optimizer and its application to feature selection

2019 ◽  
Vol 76 ◽  
pp. 16-30 ◽  
Author(s):  
Qiang Tu ◽  
Xuechen Chen ◽  
Xingcheng Liu
Keyword(s):  
Feature Selection ◽  
Grey Wolf Optimizer ◽  
Grey Wolf
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