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Improved Chaotic Grey Wolf Optimization for Training Neural Networks

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Title Improved Chaotic Grey Wolf Optimization for Training Neural Networks
 
Creator Ramana, B V
Panda, Nibedan
Teja, S
Mohapatra, Hitesh
Dalai, A K
Majhi, S K
 
Subject ANN
Chaos technique
GWO
Metaheuristic optimization
Swarm intelligence
 
Description 1193-1207
This paper introduces one improved version of the Grey Wolf Optimization algorithm (GWO), one of the newly established nature-inspired metaheuristic algorithms, and the suggested approach is termed Chaotic Grey Wolf Optimization (CGWO). The newly suggested approach CGWO is premeditated by the integration of the chaos technique with the GWO algorithm, aiming to resolve global optimization problems by maintaining a proper balance between exploration and exploitation. In the proposed approach, CGWO is assessed over the classic 23 benchmark functions. The proficiency of the freshly suggested approach, CGWO is verified by comparing it with contemporary methods as well as examined through statistical analysis also. Further, the same CGWO is utilized to train neural networks (MLP) by considering benchmark datasets, for data classification and establishing a better classifier algorithm.
 
Date 2023-11-06T09:03:35Z
2023-11-06T09:03:35Z
2023-11
 
Type Article
 
Identifier 0022-4456 (Print); 0975-1084 (Online)
http://nopr.niscpr.res.in/handle/123456789/62857
https://doi.org/10.56042/jsir.v82i11.5322
 
Language en
 
Publisher NIScPR-CSIR,India
 
Source JSIR Vol.82(11) [November 2023]