An Improved Ant Colony Optimization Algorithm Using Local Pheromone and Global Pheromone Updating Rule

By adopting the advantages of the ant colony algorithm in this paper, the modified ACO can be extended to tackle the optimization problem in continuous domain. In coding part of the modified ACO, the interval of each of the design variables in practical optimization problems is taken into consideration in the algorithm, thus the new coding strategy is quite novel. In the modified algorithm constant updating is used to simplify the local updating and the influence is reduced on the amount of pheromone increase affected by function values. And the global pheromone updating of the modified ACO can be applicable to the case that optimum is zero or negative, both maximum and minimum problems. The path check module is added in the modified ACO. The tour chosen by each ant should be check if it has been calculated after the ants complete selection in order to avoid the phenomenon that the same path is calculated repeatedly, which can improve the effectiveness of the algorithm.