  Clustering problems are considered amongst the prominent challenges in
statistics and computational science. Clustering of nodes in wireless sensor
networks which is used to prolong the life-time of networks is one of the
difficult tasks of clustering procedure. In order to perform nodes clustering,
a number of nodes are determined as cluster heads and other ones are joined to
one of these heads, based on different criteria e.g. Euclidean distance. So
far, different approaches have been proposed for this process, where swarm and
evolutionary algorithms contribute in this regard. In this study, a novel
algorithm is proposed based on Artificial Fish Swarm Algorithm (AFSA) for
clustering procedure. In the proposed method, the performance of the standard
AFSA is improved by increasing balance between local and global searches.
Furthermore, a new mechanism has been added to the base algorithm for improving
convergence speed in clustering problems. Performance of the proposed technique
is compared to a number of state-of-the-art techniques in this field and the
outcomes indicate the supremacy of the proposed technique.
