Modeling Ant Colony Algorithms Using Learning Automata

Ant colony algorithms are a group of heuristic optimization algorithms that have been inspired by ants foraging for food. In these algorithms there are some agents, the ants, that for finding the suitable solution, search the solution space. On the other hand, Learning Automata is an abstract model that can do finite actions. Each selected action is evaluated by a random environment and the environment makes a response back to Learning Automata. Using this response, Learning Automata select its action for the next step and improves its efficiency. In this paper we show that ant colony algorithms can be modeled by a group of cooperating Learning Automata and then using a group of set of cooperating learning automata an algorithm for solving the routing problem in computer networks has been proposed. Computer simulations have been conducted to show the efficiency of the proposed method.