Ant colony optimization algorithm based on asynchronous update of pheromone

To deal with the assumptions in basic ant colony optimization (AGO) algorithm that the velocities of ants are infinite and pheromones of ants are updated synchronously, an improved AGO algorithm based on asynchronous update of pheromone is proposed. The algorithm, which assumes that the velocities of ants are finite, uses an asynchronous update rule to adjust pheromone concentration of each ant, thereby the cooperation of ants is changed indirectly. It conforms to the real ants system much better and has a better global searching ability. The experiments on traveling salesman problem prove the validity, robustness and solution diversity of the new algorithm.