A Novel Sequenced Hybrid WOA-GWO Algorithm for Classification

  • Mukesh Nimbiwal, Jyoti Vashishtha


Finding optimal feature subset is a trending area for the researchers to solve various data mining problems. Hybrid swarm optimization algorithms prove their superiority in this area. This paper introduces a novel hybrid sequenced method which is obtained by using whale optimization algorithm (WOA) and grey wolf optimization algorithm (GWO)in sequence manner. This novel hybrid sequenced WOA-GWO provides a wrapper based feature selection technique. Hybridization is performed to remove the limitations of both the algorithms i.e. local stagnation and immature convergence. 11 datasets are used from UCI machine learning repository to provide significance results of proposed method. Various comparisons are performed to prove the superiority of proposed method. All the results i.e. classification accuracy, number of selected features and fitness value prove that the proposed method has superior capabilities with respect to other methods in the area of feature selection and classification accuracy.

How to Cite
Mukesh Nimbiwal, Jyoti Vashishtha. (2020). A Novel Sequenced Hybrid WOA-GWO Algorithm for Classification. International Journal of Advanced Science and Technology, 29(04), 9072 -. Retrieved from http://sersc.org/journals/index.php/IJAST/article/view/30691