Pearson correlation coefficient-based pheromone refactoring mechanism for multi-colony ant colony optimization

To solve the problem of falling into local optimum and poor convergence speed in large Traveling Salesman Problem (TSP), this paper proposes a Pearson correlation coefficient-based Pheromone refactoring mechanism for multi-colony Ant Colony Optimization (PPACO). First, the dynamic guidance mechanism is introduced to dynamically adjust the pheromone concentration on the path of the maximum and minimum spanning tree, which can effectively balance the diversity and convergence of the algorithm. Secondly, the frequency of communication between colonies is adjusted adaptively according to a criterion based on the similarity between the minimum spanning tree and the optimal solution. Besides, the pheromone matrix of the colony is reconstructed according to the Pearson correlation coefficient or information entropy to help the algorithm jump out of the local optimum, thus improving the accuracy of the solution. These strategies greatly improve the adaptability of the algorithm and ensure the effectiveness of the interaction. Finally, the experimental results indicate that the proposed algorithm could improve the solution accuracy and accelerate the convergence speed, especially for large-scale TSP instances.