• Syed Hauider Abbas
• Dr Sanjay Kumar Agarwal
• Nazish Siddiqui

Ant colony optimization is a technique for optimization that was introduced in the early 1990’s.Ant colony optimization(ACO) is one of the most recent techniques for approximate optimization.The inspiring source of ACO algorithms are real ant colonies.Ant colony optimization is a technique which can be used for various applications. Ant colony Optimization is an optimization technique that is based on the foraging behaviour of real ant colonies. Ant colony optimization is applied for the image processing which are on the basis continuous optimization. This paper deals with the various tasks of image processing which could be achieved with the help of Ant Colony Optimization (ACO) an important field of soft computing.Ant colony optimization (ACO) is a technique which can be used for various applications.Ant colony Optimization is an optimization 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. ACO, a field of SC, is a computational intelligence based approach which is used to solve combinatorial optimization problem. The simplicity and optimal approach of ACO has led its applicability to routing, scheduling, sub-set, assignment and classification problems. Focus of the this paper is onto the use of Ant Colony Optimization in the field of Image Processing. Application/Improvements: Edge detection, edge linking, feature extraction, segmentation and image compression are the various image processing tasks in which ACO has been applied successfully. The details pertaining to each of the approach have been discussed. Benefits of using ACO over the conventional techniques have also been presented. Ant colony optimization algorithms have been applied to many combinatorial optimization problems, ranging from quadratic assignment to protein folding or routing vehicles and a lot of derived methods have been adapted to dynamic problems in real variables, stochastic problems, multi-targets and parallel implementations. It has also been used to produce near-optimal solutions to the travelling salesman problem. The first ACO algorithm was called the ant system and it was aimed to solve the travelling salesman problem, in which the goal is to find the shortest round-trip to link a series of cities.The general algorithm is relatively simple and based on a set of ants, each making one of the possible round-trips along the cities.The simplicity and optimal approach of ACO has led its applicability to routing, scheduling, sub-set, assignment and classification problems.

Ant Colony Optimization (ACO), Soft Computing Image processing,, CACO (Continuous ant Colony optimization).

"Image Processing Using Ant Colony Optimization ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.946-953, June 2019, Available :http://www.jetir.org/papers/JETIR1906N46.pdf

"Image Processing Using Ant Colony Optimization ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp946-953, June 2019, Available at : http://www.jetir.org/papers/JETIR1906N46.pdf