An Improved GWO Algorithm for Data Clustering

Grey wolf optimization (GWO) is one among the most promising swarm intelligence based nature inspired meta-heuristic algorithm that improves its search process by mimicking the search for prey and attacking strategy of grey wolfs. To further improve its performance, here we have hybridized with Jaya algorithm that improves the exploration capability and hence maintains a trade between exploitation and exploration. An extensive simulation work is carried out to make a comparative analysis of our proposed method with respect to original GWO algorithm and three other meta-heuristic based clustering algorithms such as JAYA, PSO and ALO considering Accuracy, Sensitivity, Specificity and F-score performance measures. The proposed method is used to cluster each dataset taken from UCI machine learning repositories and the experiment is conducted for total 12 datasets separately. The statistical test of the proposed model is conducted by performing Friedman and Nemenyi hypothesis test and Duncan’s multiple test. The obtained results from the statistical test show the superiority of our proposed method with respect to other meta-heuristic based clustering methods.