Optimization of Large-scale Routing Problems using Ant Colony Systems on GPU Clusters

Author(s)	Prof. Dr. Shailja Shukla, Ms. Shivani Jatav
Country	India
Abstract	Optimization of large-scale routing problems is a crucial challenge in computational logistics and artificial intelligence. Ant Colony Systems (ACS), inspired by the foraging behavior of ants, have been extensively used for solving combinatorial optimization problems such as the Travelling Salesman Problem (TSP). However, solving large-scale instances of such problems requires significant computational resources. This paper explores the implementation of ACS on Graphics Processing Unit (GPU) clusters to achieve high-performance optimization. The study compares traditional CPU-based implementations with GPU-accelerated solutions, highlighting the improvements in processing speed and solution accuracy. Our results demonstrate that GPU clusters significantly enhance the efficiency of ACS-based routing problem solutions, making them a viable option for real-time and large-scale applications.
Keywords	Ant Colony Systems, GPU Clusters, Routing Optimization, Parallel Computing, Travelling Salesman Problem, Metaheuristic Algorithms, High- Performance Computing, Swarm Intelligence.
Field	Computer Applications
Published In	Volume 8, Issue 2, March-April 2026
Published On	2026-04-03

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International Journal For Multidisciplinary Research