Variance-based differential evolution algorithm with an optional crossover for data clustering

Abstract

The differential evolution optimization-based clustering techniques are powerful, robust and more sophisticated than the conventional clustering methods due to their stochastic and heuristic characteristics. Unfortunately, these algorithms suffer from several drawbacks such as the tendency to be trapped or stagnated into local optima and slow convergence rates. These drawbacks are consequences of the difficulty in balancing the exploitation and exploration processes which directly affects the final quality of the clustering solutions. Hence, a variance-based differential evolution algorithm with an optional crossover for data clustering is presented in this paper to further enhance the quality of the clustering solutions along with the convergence speed. The proposed algorithm considers the balance between the exploitation and exploration processes by introducing (i) a single-based solution representation, (ii) a switchable mutation scheme, (iii) a vector-based estimation of the mutation factor, and (iv) an optional crossover strategy. The performance of the proposed algorithm is compared with current state-of-the-art differential evolution-based clustering techniques on 15 benchmark datasets from the UCI repository. The experimental results are also thoroughly evaluated and verified via non-parametric statistical analysis. Based on the obtained experimental results, the proposed algorithm achieves an average enhancement up to 11.98% of classification accuracy and obtains a significant improvement in terms of cluster compactness over the competing algorithms. Moreover, the proposed algorithm outperforms its peers in terms of the convergence speed and provides repeatable clustering results over 50 independent runs.