Study of delivery path optimization solution based on improved ant colony model

摘要

As a bionic optimization algorithm, ant colony algorithm has the advantages of robustness, parallel computation and easy combination and so on, which can solve complicated combinatorial optimization problems. However, the selection strategy of traditional algorithm is more random, which leads to the slow evolution speed. Therefore, an improved ant colony algorithm is proposed, which uses density peak clustering algorithm to classify sites and local optimization strategy. The simulating results of multiple TSP problems demonstrate that the improved algorithm has good optimization ability, greatly improves the quality of the solution and the speed of optimization, and overcomes the slowness and tendency of the algorithm.