Self Adapting Differential Search Strategies Improved Artificial Bee Colony Algorithm-Based Cluster Head Selection Scheme for WSNs

The process of cluster head selection under the process of cluster formation in wireless sensor networks is determined to be essential for extending the lifetime of the network. In this paper, a Self Adapting Differential Search Strategies Improved Artificial Bee Colony Algorithm (SADSS-IABCA)-based Cluster Head Selection Scheme is proposed for prolonging the lifetime of the network with improved Quality of Service. The differential search strategies employed in the SADSS-IABCA-based Cluster Head Selection Scheme are reliable in updating the dependent variables in periodic intervals of time through the integration of mutation and crossover. This proposed SADSS-IABCA-based Cluster Head Selection Scheme incorporated diversified search strategies associated with differential evolution with the employee and onlooker bee phase in order to improve the local searching ability of ABC with the view to eliminate its limitations of delayed convergence. In addition, the appropriate selection of differential evolution strategies is computed through the probability-based self adapting process for effective selection of cluster heads. The simulation results of the proposed SADSS-IABCA-based Cluster Head Selection Scheme also confirmed a predominant improvement in percentage of alive nodes, throughput and mean residual energy compared to the benchmarked cluster head schemes used for investigation.