An adaptive ant colony algorithm based on common information for solving the Traveling Salesman Problem

Abstract

Ant colony algorithm has been successfully applied to the Traveling Salesman Problem (TSP). But it has some disadvantages, such as easily plunging into local minimum, slow convergence speed and so on. In order to find the optimal path accurately and rapidly, an improved ant colony algorithm is proposed. The improved algorithm strengthens the consideration of the common information to induce ant colony to the local search and reduce the redundant operations. Moreover, improved algorithm uses adaptively adjusting pheromone decay parameter mechanism to adjust convergence rate and ensure the global search ability. Experiments show that the algorithm has a remarkable quality of convergent precision and the convergent velocity.