Improving Optimization Prowess of Ant Colony Algorithm Using Bat Inspired Algorithm

Abstract

Metaheuristic algorithms such as Ant Colony Optimization (ACO) algorithm and Bat Optimization Algorithm (BOA) have been widely employed in solving different optimization problems in several fields. ACO is modelled based on the social behaviour of ants that look for appropriate answers to a given optimization issue by recasting it as the case of locating the least expensive path on a weighted graph. A set of parameters linked to graph components (either nodes or edges) whose values are changed by the ants during runtime constitute the pheromone model, which biases the stochastic solution generation process. However, the effectiveness of ACO declines as the quantity of packets rises, making them ineffective for reducing traffic congestion. As more packets are transmitted, their strength decreases, causing packet congestion, rendering them useless for reducing packet traffic congestion. On the contrary, BOA which was modelled after the behavior of bats has also been employed in fixing network routing issues by listening to every sound in a space and taking note of what is going on around it. In order to further improve ACO algorithm and decrease packet traffic congestion, packet loss, and the time it takes a packet to reach its destination in a network system, this study employs the strength of BOA. Results obtained revealed the prowess of BOA in improving the performance of ACO for network packet routing.