A solution of TSP based on the improved ant colony optimization

In recent years, self-driving delivery vehicles have been used more and more widely. The route planning of courier vehicles can be abstracted as the traveling salesman problem (TSP). For the courier vehicle path optimization problem, an improved population-based ant colony optimization algorithm (IPACO) is proposed. The ant colony optimization algorithm (ACO) is a swarm intelligent bionic algorithm with the advantages of positive feedback, robustness, and easy combination with other algorithms, but it also has the problems of low solution accuracy and easy to fall into local optimality. In order to avoid these problems, the 2-opt local optimization operator is combined in the algorithm search process to improve the diversity of the population. In addition, the property that the simulated annealing algorithm probabilistically accepts relatively poor solutions is used to optimize the optimal ants during the iterative process. Finally, some TSPLIB examples are selected to verify the performance of the algorithm, and the fast adaptation capability of the algorithm under the change of path node weights is verified by simulation.