Ant Colony Optimization Towards Image Processing | Original Article

ABSTRACT:

Ant Colony Optimization (ACO) is an optimization algorithm inspired by ant colonies. In nature, ants of some species initially wander randomly until they find a food source and return to their colony laying down a pheromone trail. Soft Computing refers to the techniques of problem solving which are inspired from the human behavior, natural genetics and the behavior of insects. Ant colony optimization (ACO) is a technique which can be used for various applications. All these techniques are parallel computational techniques which aim to handle imprecise, incomplete, non-linear and complex data. This paper deals with one of the fields of soft computing- namely Ant Colony Optimization (ACO). ACO is a computational intelligence based approach which is used to solve combinatorial optimization problem. Due to its simplicity and optimal approach it has been applied to routing, scheduling, sub-set, assignment and classification problems. Focus of the current paper is onto the use of Ant Colony Optimization in the field of Image Processing. The details pertaining to each of the approach have been discussed. This paper proposes an ant colony optimization (ACO) based algorithm for continuous optimization problems on images like image edge detection, image compression, image segmentation, structural damage monitoring etc in image processing .This paper represents that how ACO is applied for various applications in image processing. The algorithm can find the optimal solution for problem.