Enhanced flower pollination algorithm on data clustering
Parul Agarwal, Shikha Mehta · International Journal of Computers and Applications · 2016
Nature-inspired algorithms are emerging as most compatible algorithms in obtaining near-optimal solution to complex problems. The ability of meta-heuristic algorithm to obtain global optimization solution largely depends on convergence behavior. In order to enhance convergence capability of latest nature-inspired algorithm i.e. flower pollination algorithm (FPA), a modified version is presented. The performance of modified FPA is tested over clustering application. Algorithm is assessed in contrast to bat algorithm, firefly algorithm, and conventional FPAs on 10 clustering data-sets. Out of 10 data-sets, 8 are derived from pattern recognition and 2 are artificially generated. Clustering results are computed in terms of objective function value and CPU time taken at each run. Run length distribution graphs illustrate the convergence behavior of algorithms. Results indicate that the proposed modified FPA outperforms its counterparts both in terms of attaining best fitness value and reducing the CPU time.