A Study of Parallel Ant Colony Optimization with CUDA-Based Implementation

This paper introduces Ant Colony Optimization (ACO) algorithms implemented using CUDA technology for parallelization, inspired by the intelligent behavior of ants in nature. The goal is to demonstrate their efficiency in real-world applications, particularly in improving vehicle routing and scheduling in transportation and logistics. The ACO algorithm has proven to enhance operational efficiency and increase customer satisfaction. By integrating ACO principles with CUDA parallel computing, their performance has been extended to include domains like electromagnetic scattering computations and microwave antenna designs.