Ant Colony Optimization (ACO) studies artificial systems that take inspiration from the behavior of real ant colonies and which are used to solve discrete optimization problems. Ants don’t use vision as they are Almost blind. Shortest path is discovered via pheromone trails, which is the chemical left on the ground which is use as a signal for other ants. More pheromone on path increases probability of path being followed. When ants want to reach to the food from home they wander randomly through different paths and when any one ant finds a short path from colony to food source, other ants are more likely to follow that short path, and such positive feedback eventually leaves all ants following same path. They are Incapable of achieving complex tasks alone and rely on the phenomena of swarm intelligence for survival. ACO originally applied to Traveling Salesman Problem and Applied later to various hard optimization problems and also use for Solving NP-hard combinatorial problems.