Bayesian-Based Ant Colony Optimization Algorithm for Edge Detection

Ant colony optimization (ACO) is a random search algorithm based on probability calculation. However, the uninformed search strategy has a slow convergence speed. The Bayesian algorithm uses the historical information of the searched point to determine the next search point during the search process, reducing the uncertainty in the random search process. Due to the ability of the Bayesian algorithm to reduce uncertainty, a Bayesian ACO algorithm is proposed in this paper to increase the convergence speed of the conventional ACO algorithm for image edge detection. In addition, this paper has the following two innovations on the basis of the classical algorithm, one of which is to add random perturbations after completing the pheromone update. The second is the use of adaptive pheromone heuristics. Experimental results illustrate that the proposed Bayesian ACO algorithm has faster convergence and higher precision and recall than the traditional ant colony algorithm, due to the improvement of the pheromone utilization rate. Moreover, Bayesian ACO algorithm outperforms the other comparative methods in edge detection task.