Bioinspired Clustering in UWSNs Using Monarch Butterfly Optimization Compared with Ant Colony Optimization Algorithm

Abstract

The Monarch Butterfly Optimization (MBO) algorithm for clustering in Underwater Wireless Sensor Networks (UWSNs) is investigated and compared to the performance of the Ant Colony Optimization (ACO) algorithm in this paper. As is evident from the outcomes depicted in the above graphs MBO consumes an average of 31% less energy than the ACO while lasting longer in the network. 7J or make a network get 790 round life cycle while ACO did 34. 5J and 730 rounds. The results indicate that MBO is more advantageous to cluster in the UWSNs making it a viable solution to boost up the network productivity and the overall energy utilization efficiency.