An Enhanced GWO Algorithm with Improved Explorative Search Capability for Global Optimization and Data Clustering

In this paper, we have introduced a differential perturbation operator into the gray wolf optimization (GWO) algorithm using three randomly selected omega wolves which assist the three leader wolves of the original GWO algorithm for diversifying the solution quality among the feasible omega wolves. Additionally, we have introduced the use of similar values for the control parameters (A and C) of GWO for each leader wolf while updating the position of a single omega wolf. This diversification among the omega wolves introduces an element of exploration in the exploitation phase and hence further improves the optimization capability of the GWO algorithm. For comparative performance analysis, the results obtained from the proposed algorithm are compared with ten promising recently proposed meta-heuristic algorithms such as IAOA, RSA, mGWOA, VWGWO, mGWO, GWO, SCA, JAYA, ALO and WOA in optimizing 23 mathematical benchmark unimodal, multimodal and fixed dimension functions. Additionally, the performance of the proposed algorithm is tested in 12 promising data clustering problems using four performance measures such as accuracy, precision, F-score and MCC. Superiority of the proposed algorithm in optimizing benchmark functions and data clustering is statistically verified using pairwise Wilcoxon signed-rank test and Friedman and Nemenyi hypothesis test.