30-08-2017, 03:15 PM
Ant Colony Optimization (ACO) is an optimization algorithm inspired by the natural behavior of the ant species that ants deposit in the soil to ferment. In this article, ACO is presented to address the image edge detection problem. The proposed ACO based edge detection approach is capable of establishing a pheromone matrix representing the edge information presented at each pixel position of the image, according to the motions of a number of ants being sent to move about the picture. In addition, the movements of these ants are driven by the local variation of the values of intensity of the image. Experimental results are provided to demonstrate the superior performance of the proposed approach.
Ant colony optimization (ACO) is an optimization algorithm inspired by nature that is motivated by the natural foraging behavior of ant species. Ants deposit pheromones in the soil to mark paths between a food source and its colony, which must be followed by other members of the colony. Over time, the pheromone trails will evaporate. The longer an ant takes to travel down the road and back again, the longer the pheromones have to evaporate. Shorter-and therefore favorable-paths move faster and receive greater compensation for pheromone evaporation. Pheromone densities remain high on shorter trajectories because the pheromone is deposited more rapidly. This positive feedback mechanism eventually leads to ants following the shortest paths. It is this natural phenomenon that inspired the development of metaheuristic ACO. Dorigo et al. proposed the first ACO algorithm, an ant system (AS). Since then, extensions to AS have been developed, such as the ant colony system (ACS) and the MAX-MIN ant system (MMAS). ACO has been used to solve a wide variety of optimization problems.
Ant colony optimization (ACO) is an optimization algorithm inspired by nature that is motivated by the natural foraging behavior of ant species. Ants deposit pheromones in the soil to mark paths between a food source and its colony, which must be followed by other members of the colony. Over time, the pheromone trails will evaporate. The longer an ant takes to travel down the road and back again, the longer the pheromones have to evaporate. Shorter-and therefore favorable-paths move faster and receive greater compensation for pheromone evaporation. Pheromone densities remain high on shorter trajectories because the pheromone is deposited more rapidly. This positive feedback mechanism eventually leads to ants following the shortest paths. It is this natural phenomenon that inspired the development of metaheuristic ACO. Dorigo et al. proposed the first ACO algorithm, an ant system (AS). Since then, extensions to AS have been developed, such as the ant colony system (ACS) and the MAX-MIN ant system (MMAS). ACO has been used to solve a wide variety of optimization problems.