Combinatorial optimization problems naturally arise in the industrial world all the time. Often, having sufficient solutions to these problems can save companies millions of dollars. We know of many examples in which optimization problems arise; for example: bus scheduling, telecommunication network design, travelling, and vehicle routing problems. Perhaps the most famous combinatorial optimization problem is the travelling salesman problem (TSP). The TSP can be simply thought of as the problem of figuring out a tour of cities a salesman must travel (visiting each city exactly once) so that the total distance travelled is minimized.
With the evergrowing arise of combinatorial optimization problems and their intrinsic link to industrial problem, many researchers, mathematicians and computer scientists, have developed a plethora of algorithms to solve these problems. We now distinguish the two types of algorithms; complete and approximate. Complete algorithms are able to solve these problems in such a way that in the end, the optimal solution is given. However, since many of these problems are N P-hard the optimal solution may take a long time to obtain. This is the reason why there are approximate algorithms. Approximate algorithms are designed, as expected, to give approximately the optimal solution. The great thing about using an approximate algorithm is that they can obtain good results in a relatively short amount of time.
Ant colony optimization (ACO) algorithms are some of the most recent class of algorithms designed to approximate combinatorial optimization problems. The algorithm behaves similar to real ants and their biological abilities to find the nearest food source and bring it back to their nest. The main source of communication between ants is the depositing of chemically produced pheromone onto their paths. It is with this key idea that Marco Dorigo and colleagues were inspired to create this new class of algorithms.