Solving Travelling Salesman Problem Using Ant Colony Optimization Algorithm

In this paper, we evaluate the usefulness of Ant Colony Optimization (ACO) in tackling combinatorial optimization issues, focusing mainly on the Traveling Salesman Problem (TSP). We present a full investigation of the ACO algorithm, outlining its essential components, including ant movement, pheromone update, and solution construction. Through testing on diverse datasets, we examine the algorithm’s performance and explore its convergence behavior. Additionally, we do sensitivity analysis to assess the effect of alternate parameter values on solution quality and convergence. Our results show the efficacy of the ACO technique in developing high-quality solutions for TSP scenarios and illustrate its robustness to parameter changes. Overall, this work brings substantial insights into the use of ACO in optimization issues and offers practical aid for parameter adjustment and algorithm implementation.