Spark-based Improved Ant Colony Algorithm for Solving TSP Problems

In this paper, a Spark-based improved ant colony algorithm (Spark-ACO) is proposed for solving the traveler’s problem (TSP). Aiming at the problems of slow convergence speed and low solution accuracy of the traditional ACO algorithm, the efficient parallelization of pheromone updating is achieved by introducing the Spark parallel computing framework, which significantly improves the performance of the algorithm. In addition, combining the 2 -opt local optimization technique and the dynamic pheromone adjustment strategy further improves the algorithm’s search capability and the quality of the solution. Experimental results show that Spark-ACO performs well on several standard datasets such as Att48, Eil76, and kroA100, and not only finds the optimal solution quickly, but also significantly outperforms the comparison algorithms in terms of solution time and stability. The algorithm is especially suitable for the fast solution of large-scale TSP problems, providing an efficient and reliable solution for practical applications.