Application of Ant Colony Optimization to Solving the Traveling Salesman's Problem

Ant colony optimization algorithm was developed by Marco Dorigo in 1991 as an algorithm for solving optimization problems in computer applications. This algorithm models the behavior of natural ants' colonies and has been used exclusively for solving problems in the discrete domain. This article fully implements and evaluates a specialized version of Ant Colony Optimization (A.C.O) capable of solving the Traveling Salesman's Problem (T.S.P) using the Object Modeling Technique (O.M.T) and evaluates its performance under a range of conditions and test cases. The work highlighted in this article has shown that a number of cooperating artificial ants using pheromone trails as a method of communication is capable of solving both simple and obviously difficult optimization problems with encouraging results. The Traveling Salesman's Problem arises as a sub-problem in many transportation and logistics applications, such as the routing of packets in a networking environment, delivery of meals to homebound people, arranging school bus routes to pick up children in a large city, routing of trucks to pick up parcels, the scheduling of stalker cranes in a national sea or airports and all such similar cases (10,12) .