An omega tuned grey wolf optimization applied in binary classification problems
Akash Punhaniakashpunhani@gmail.comSchool of Computer Science and EngineeringIILM UniversityGreater Noida, Uttar Pradesh, 201306, India0000-0002-4693-498XView full profile → , *Sumit KumarCorresponding authorsumitnadar@gmail.comSchool of Computer Science and EngineeringIILM UniversityGreater Noida, Uttar Pradesh, 201306, India0000-0002-9494-1244View full profile → , Lalit Kumarlalitkumar@iilm.eduSchool of Computer Science and EngineeringIILM UniversityGreater Noida, Uttar Pradesh, 201306, India0000-0002-5132-4561View full profile →
* Corresponding author · click or hover a name for details
- Received:
- 01 Dec 2025
- Published Online:
- 29 Aug 2026
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIOS-2313
- Pages:
- 1–13
Abstract
Popular and latest technology which is named as grey wolf optimizer (GWO) is very advantageous in hunting the grey wolves due to its meta heuristic behaviour of algorithm. In this algorithm the hunting process of the grey wolves have been defined based on the hierarchy of the wolves. However, in this hierarchical structure there has been no definition given to the wolves at the lowest level. These wolves are termed as omega wolves; they are termed to assist all the three categories of wolves. In this paper, we have modelled their role in the hunting process which was not considered in the previously proposed models. The definition for the contribution of Delta, Beta and Alpha wolves is modified in this approach. The proposed approach is tested on CEC 2017 benchmarks and proved to be more efficient in 12 out of 29 benchmarks for 30 Dimensions than its state of art variants. The proposed approach is used for classification with the help of multi-perceptron.
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References
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