Ant colony optimization (ACO) is a feasibility heuristic optimization technique inspired by pheromone trail laying and continuous following behavior of some real ant species that search and find the nearby shortest paths from the colony to take their own food. This technique tacks discrete optimization problems that can be diminished to finding a good pathway through graphs. ACO algorithm is a particle swarm-based optimization algorithm with the global optimization ability in its fuzzy system with fuzzy reasoning competence. Therefore, ACO has greatly improved in,

 •  Convergence speed
 •  Expanded search space
 •  Enhanced optimization ability to a given certain extent.

ACO is strong enough for parallel computing, heuristic search space, and an easy combination with other methods to solve highly complex optimization problems. Some challenges of Ant colony Optimization algorithm attempts such as,

 •  Online Realization
 •  Robustness
 •  Generalization
 •  Disaster problem

ACO application has been thrivingly applied to a vast range of numerous complex combinatorial optimization problems such as,

 •  Traveling salesman problem
 •  Vehicle routing problem
 •  Quadratic assignment problem
 •  Problem of network-traffic optimization
 •  Problem of graph coloring
 •  Job-shop scheduling problem
 •  Combinatorial optimization
 •  Industrial design
 •  Telecommunication
 •  Bio-informatics