Ant Colony Optimization based on Pheromone Trail Centralization

Aiming at the disadvantage (premature convergence) of the ant colony optimization (ACO), a mechanism called pheromone trail centralization (PTC) is presented. The mechanism adjusts the pheromone trails proportionally and facilitates the exploration by increasing the probability of selecting solution components with low pheromone trail. It can avoid premature convergence of ACO and exploit more strongly solutions. The results show that ACO with PTC are superior to the existing ACO and the mechanism is useful to improve the performance of any versions of ACO by investigating the functioning of PTC in the traveling salesman problem (TSP)