Improved Ant Colony Algorithm And Its Application Research In Image Edge Detection

Owing to the image is often seen as the large amount of data matrix which composed of its pixels,image edge detection can be summed up in solving combinatorial optimization problems in math.And the advantages of ant colony algorithm with the positive feedback,robustness, and distributed processing is easier to deal with complex, combinatorial optimization problems which with large amount of data or some problems which can be converted into combinatorial optimization problem.So it is an effective exploration and attempt using the ant colony algorithm for image edge detection.In view of the traditional ant colony algorithms of image edge detection with its disadvantages of poor effect in image edge detection, and the algorithm is easy to fall into local optimum and the imbalance algorithm in random and positive feedback mechanism, and too slow convergence speed,so an improved ant colony algorithm have been proposed.Improved ant colony algorithm change the initial position of the ant placed from randomly into placed near the edge points, At the same time the tabu table join in the algorithm in the initialization stage,and using classical Canny edge detection operator to get the information as heuristic information of ant movement,and setting up edge tracking model based on ant colony algorithm, and also doing an adaptive adjustment for the pheromone volatilization rate ?,and the new pheromone updating formula are obtained.Finally,simulation experiments futher to verify the validity of this improved algorithm.Simulation test shows that the changes in the distribution of the initial position, higher probability to make it become the edge pixels as a starting point for global search, iterative process is more applied to the edge of the local looking when the area of high probability detection is proceeding and improve the efficiency of algorithm;And the joining tabu table helps to enhance the ability for ant to find the optimal solution at the same time to avoid theants pointless moving back and forth within the scope of the local search; The setting up of the edge tracking model based on ant colony algorithm realized the guiding role of edge tracking for pheromone and heuristic information, This will avoid the ants walking and distribution in the non-marginal area,and improved algorithm has also solved the traditional algorithm problem which has the imbalance problems in random and positive feedback mechanism in algorithm.and positive feedback mechanism of coordination problem. which overcomes the shortcoming of local optimum. Adaptively changing the pheromone volatilization rate coefficient ? value will improve the global search ability of the algorithm,Pheromone volatilization rate ? adjustment is to avoid the algorithm trapped in local optimal or stagnant,At the same time,with better changes of the value of ? will improve convergence speed of the algorithm.In short, the improved algorithm were superior in image edge detection effect or algorithm convergence speed to the traditional ant colony image edge detection algorithm,So as to achieve the improvement purpose.