Improved Chaotic Grey Wolf Optimization for Training Neural Networks: IMPROVED CGWO FOR TRAINING NEURAL NETWORKS

Ramana Venkata Bendi; Nibedan Panda; S Teja; Hitesh Mohapatra; Asish Dalai; Santosh Kumar Majhi

doi:10.56042/jsir.v82i11.5322

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

DOI:

https://doi.org/10.56042/jsir.v82i11.5322

Keywords:

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

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.