New ant colony optimization algorithm based on supervisory mechanism

Given such problems as slow convergence speed and premature convergence existing in basic colony optimization algorithm,and enlightened by supervisory mechanism,the supervisor ant colony optimization(SACO)algorithm was introduced.With the supervisory distance as an evaluation criterion,SACO self-adaptively adopted excellent ants to update pheromone trails,thus improving the solution qualities of each iteration,and a better guide was made for the ants later.The optimized global pheromone trail strategy was selected in the prophase of the evolution in SACO,and the pheromone trail was added more, whereas in the anaphase the pheromone trail was added less.Moreover,the pheromone trail was adaptively limited to a certain range,avoiding the selecting probability of a path being too large or too small.When the SACO converges to an optimal solution,the exploring probability is adaptively increased,which helps to jump out of the local optimal solution.The simulation experiments show that SACO not only obtains stable and optimal solutions but also enhances the convergence speed.