Abstract:

In view of the stagnation behavior of ant colony optimization (ACO) algorithm, this paper proposes and implements a new dynamic transition and search strategy. The artificial ants are partitioned into several groups. Each group of ant colony releases different types of pheromones. Attract factor and exclusion factor are introduced, and a new transition probability with multiple ant colony is given so as to strengthen the global search capability. By tackling symmetric travelling salesman problems (TSP), this paper compares the improved algorithms implementation with the existing algorithms. The experimental results indicate that the improved algorithm is superior to the ACO and ant colony system, ACS algorithms. The improved algorithm has excellent global optimization properties and the faster convergence speed, and it can avoid premature convergence of ACO.