Threshold Binary Grey Wolf Optimizer Based on Multi-Elite Interaction for Feature Selection-Reference-Cited by-同舟云学术

Threshold Binary Grey Wolf Optimizer Based on Multi-Elite Interaction for Feature Selection

Published:2023 Issue: Volume:11 Page:34332-34348
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Wu Hongzhuo¹^ORCID,Du Shiyu²,Zhang Yiming²,Zhang Quan¹,Duan Kai¹,Lin Yanru¹

Affiliation:

1. Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, China

2. Engineering Laboratory of Advanced Energy Materials, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China

Funder

National Natural Science Foundation of China

Entrepreneurship Program of Foshan National Hi-Tech Industrial Development Zone and Zhejiang Province Key Research and Development Program

“Pioneer” and “Leading Goose” Research and Development Program of Zhejiang

International Partnership Program of the Chinese Academy of Sciences

Leading Innovative and Entrepreneur Team Introduction Program of Zhejiang

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10089432.pdf?arnumber=10089432

Reference56 articles.

1. A simulated annealing algorithm with a dual perturbation method for clustering

2. Opposition-based learning Harris hawks optimization with advanced transition rules: principles and analysis

3. A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms

4. A novel Random Walk Grey Wolf Optimizer

5. Hybrid Particle Swarm and Grey Wolf Optimizer and its application to clustering optimization

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