A Comparative Analysis of Genetic Algorithm and Ant Colony Optimization for Mobile Augmented Reality Offloading.

Mobile augmented reality (MAR) systems typically offload computation-intensive tasks to edge servers. This paper contributes a comparative analysis of Genetic Algorithm (GA) and Ant Colony Optimization (ACO) in task offloading optimization for MAR on the edge, in terms of latency and power consumption across eight distinct setups with varying server configurations and tasks. Our experimental findings indicate that the ACO algorithm consistently manifests lower latency than the GA algorithm in setups where the servers are in proximity to the edge devices. However, the GA outperforms the ACO algorithm in setups where the servers are situated at a greater distance. The ACO algorithm primarily offloads tasks to a single server, resulting in consistent latency performance, while the GA demonstrates a more diverse task offloading strategy by distributing tasks across multiple servers and the local device. The latency fluctuations in the ACO are primarily attributed to changes in the offloading patterns, especially when tasks are offloaded to different servers than those predominantly used. On the other hand, the GA algorithm, with its more varied offloading approach, exhibits less significant latency variations. Regarding to power consumption, the GA algorithm generally consumes higher power than the ACO algorithm in most setups due to its more diverse task distribution strategy.