Inversed Control Parameter in Whale Optimization Algorithm and Grey Wolf Optimizer for Wrapper-based Feature Selection: A comparative study

Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO) are well-perform metaheuristic algorithms used by various researchers in solving feature selection problems. Yet, the slow convergence speed issue could demote performance of classification accuracy. Therefore, to overcome this issue, a modified WOA (mWOA) GWO (mGWO) for wrapper-based were proposed study. The mWOA mGWO given new inversed control parameter which was expected enable more search area agents early phase resulted faster speed. objective comparative study is investigate compare effectiveness methods against original terms number selected features implemented MATLAB where 12 datasets with different dimensionality from UCI repository used. kNN chosen as classifier evaluate accuracy features. Based on experimental results, did not show significant improvements reduction maintained similar GWO. On contrary, outperformed two criteria mentioned even high-dimensional datasets. Evaluating execution time methods, utilizing classifiers, hybridizing other solve problems would be future works worth exploring.