Hybrid Artificial Bee Colony and Monarchy Butterfly Optimization Algorithm (HABC-MBOA)-based cluster head selection for WSNs

Abstract

Energy efficiency is considered as the most potential issue in Wireless Sensor Networks (WSNs), since they incorporate limited sized batteries that could not be recharged or replaced. The energy possessed by the sensor nodes needs to be optimally utilized in order to extend the lifetime expectancy with guaranteed QoS in the network. In this paper, a Hybrid Artificial Bee Colony and Monarchy Butterfly Optimization Algorithm (HABC-MBOA)-based Cluster Head Selection Scheme is proposed for the predominant selection of cluster heads under clustering process. This proposed HABC-MBOA replaces the employee bee phase of ABC with mutated butterfly adjusting operator of MBOA for preventing earlier trapping of solutions into a local optimal point and delayed convergence by maintaining the tradeoff between exploitation and exploration. This proposed HABC-MBOA plays an anchor role in eliminating inadequacy of ABC algorithm towards global search potential. This proposed HABC-MBOA also eliminates the possibility of cluster heads being overloaded with maximum number of sensor nodes, that results in rapid death of the sensor nodes during the deployment of impotent cluster head selection process. The simulation results confirmed that the number of alive nodes in the network is determined to be 18.92% superior to the benchmarked cluster head selection approaches.

Keywords

• Artificial Bee Colony
• Butterfly Adjusting Operator
• Employee Adjusting Bee Phase
• Cluster Head