Genetic Algorithm vs Ant Colony Optimization for Offloading in Mobile Augmented Reality

This study presents a comparative analysis of two prominent optimization techniques, Genetic Algorithm (GA) and Ant Colony Optimization (ACO), for offloading tasks in Mobile Augmented Reality (MAR) environments. MAR applications often require intensive computational resources, leading to performance bottlenecks on resource-constrained mobile devices. Offloading tasks to remote servers can alleviate these constraints, but the selection of appropriate offloading strategies is crucial for efficient execution. GA and ACO have been widely employed in optimization problems, yet their effectiveness in the context of MAR offloading remains unexplored. Through experimentation and performance evaluation, this study aims to provide insights into the comparative effectiveness of GA and ACO for MAR offloading scenarios. The findings of this research can inform the selection of suitable optimization techniques to enhance the performance and resource utilization of MAR applications

Algorithm · ANT · Ant colony · Ant colony optimization algorithms · Augmented reality · Computer network · Genetic algorithm · Machine learning · Mathematical optimization · Augmented Reality Applications · Cloud Computing and Resource Management · Computer Science · IoT and Edge/Fog Computing · Mathematics · Artificial Intelligence