An Ant Colony Optimization Algorithm based on automatic dynamic updating

Currently, the general ant colony algorithm is disadvantage of solving continuous problem, such as the slow convergence and stagnation. To this end, we proposed an Ant Colony Optimization Algorithm which is capable of automatic dynamic updating the parameters. It chooses the ants through the fitness function (i.e., the objective function). Then, in accordance with the specific issue of the characteristics of the problem, algorithms parameters can be automatically adjusted to the optimum to make the entire optimization process. The concrete method is to transfer the discrete problem to continuous space problem through the transition probability in order to enhance the optimal path of the pheromone of ants, accelerate the convergence and avoid algorithm stagnation by controlling residual amount of pheromone. Simulation results show that the algorithm for solving the problem of continuous time domain can significantly improve the convergence speed and solution accuracy.