An Improved Two-Dimensional Entropic Thresholding Method Based on Ant Colony Genetic Algorithm

The conventional two-dimensional (2-D) entropic thresholding is time consuming due to the exhaustive search in 2-D space. An improved 2-D entropic thresholding method based on ant colony genetic algorithm is proposed. This method extends ant colony genetic algorithm to 2-D discrete space optimization and includes the conventional 2-D entropic thresholding method. In this method, the ant is at the same time the chromosome. To reflect the collaboration of ants, the 2-D entropy of the ant as well as the pheromone is used to construct the fitness function. The best threshold vector is obtained by the genetic evolution of ant colony. Experiments show that the accuracy, stability and search efficiency of this method are better than that of the 2-D entropic algorithm based on genetic algorithm or ant colony optimization.