Abstract
Enhanced shuffled bat algorithm (EShBAT) is a recently proposed variant of bat algorithm (BA) which has been successfully applied for numerical optimisation. To leverage the optimisation capabilities of EShBAT for clustering, HESB, a hybrid between EShBAT, K-medoids and K-means is proposed in this paper. EShBAT works by dividing the population of bats into groups called memeplexes, each of which evolve independently according to BA. HESB improves on that by employing K-medoids and K-means to generate a rich starting population for EShBAT. It also refines the memeplex best solutions at the end of every generation by employing K-means algorithm. Both these modifications combined together produce an efficient clustering algorithm. HESB is compared to BA, EShBAT, K-means and K-medoids, over ten real-life datasets. The results demonstrate the superiority of HESB.
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