Multi-threading based implementation of Ant-Colony Optimization algorithm for image edge detection

Ant Colony Optimization (ACO) is a nature inspired algorithm for solving optimization problems and is proved to be a powerful tool in image processing. It works on the principle that an ant while moving leaves pheromones on its path, which is used as guide to be followed by other ants. ACO is complex and time consuming. In this paper, a multi-threading based implementation of ACO is proposed for identifying edges in images. It combines multi-threading with ACO for increasing the randomness among the artificial ants. The algorithm is implemented and its performance is measured in terms of time complexity. Simulation results show that the proposed method has significantly lower execution time as compared to conventional ACO for edge detection.