Search

Aiming at the problem of slow search speed and insufficient solution quality of ant colony algorithm, this paper proposed an ant colony algorithm combining dynamic hierarchical clustering and neighborhood interval reorganization. In the ini-tial stage, the algorithm adjusted the hierarchical clustering threshold and iterated to the target cluster based on the principle of minimum inter-cluster distance merging, and then merged the clusters according to the pre-merging coefficient. In the next place it used ant colony system to generate subclass paths, disconnected and reorganized the initial paths to improve the overall convergence speed of the algorithm. Then the algorithm used ant colony system to optimize the solution space, and at the same time, carried out neighborhood interval diffusion reorganization between clusters to increase the diversity of solutions. Furthermore, if the quality of the reconstructed solution was better than the current optimization solution at a fixed number of iterations, the recommendation process would be carried out to improve the accuracy of the solution. When the algorithm stagnated, it introduced an adjustment factor to reduce the differences between pheromones of each path to enhance the ant search ability, which could help the algorithm jump out of the local optimal. The experiment result shows that the accuracy of the algorithm is about 3% when facing large-scale problems, which can effectively improve the accuracy and convergence rate of the solution. [ABSTRACT FROM AUTHOR]