A Novel Energy Aware Node Clustering Algorithm for Wireless Sensor Networks Using a Modified Artificial Fish Swarm Algorithm

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.

Empirical Study of Artificial Fish Swarm Algorithm

Artificial fish swarm algorithm (AFSA) is one of the swarm intelligence optimization algorithms that works based on population and stochastic search. In order to achieve acceptable result, there are many parameters needs to be adjusted in AFSA. Among these parameters, visual and step are very significant in view of the fact that artificial fish basically move based on these parameters. In standard AFSA, these two parameters remain constant until the algorithm termination. Large values of these parameters increase the capability of algorithm in global search, while small values improve the local search ability of the algorithm. In this paper, we empirically study the performance of the AFSA and different approaches to balance between local and global exploration have been tested based on the adaptive modification of visual and step during algorithm execution. The proposed approaches have been evaluated based on the four well-known benchmark functions. Experimental results show considerable positive impact on the performance of AFSA.