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Improved Chaotic Grey Wolf Optimization for Training Neural Networks


Affiliations
1 Department of Information Technology, Aditya Institute of Technology and Management, Tekkali 532 201, Andhra Pradesh, India
2 School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar 751 024, Odisha, India
3 School of CSE, VIT, Amaravati 522 037, Andhra Pradesh, India
4 Department of CSE, Veer Surendra Sai University of Technology, Burla 768 018, Odisha, India
 

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.

Keywords

ANN, Chaos technique, GWO, Metaheuristic optimization, Swarm intelligence.
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  • Improved Chaotic Grey Wolf Optimization for Training Neural Networks

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Authors

B. V. Ramana
Department of Information Technology, Aditya Institute of Technology and Management, Tekkali 532 201, Andhra Pradesh, India
Nibedan Panda
School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar 751 024, Odisha, India
S. Teja
Department of Information Technology, Aditya Institute of Technology and Management, Tekkali 532 201, Andhra Pradesh, India
Hitesh Mohapatra
School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar 751 024, Odisha, India
A. K. Dalai
School of CSE, VIT, Amaravati 522 037, Andhra Pradesh, India
S. K. Majhi
Department of CSE, Veer Surendra Sai University of Technology, Burla 768 018, Odisha, India

Abstract


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.

Keywords


ANN, Chaos technique, GWO, Metaheuristic optimization, Swarm intelligence.

References