Improvement of Routing Operation Based on Learning with Using Smart Local and Global Agents and with the Help of the Ant Colony Algorithm

Routing in computer networks has played a special role in recent years. The cause of this is the role of routing in a performance of the networks. The quality of service and security is one of the most important challenges in routing due to lack of reliable methods. Routers use routing algorithms to find the best route to a particular destination. When talking about the best path, we consider parameters like the number of hops, change times, and communication cost of sending data packet. In this study we will try to improve the routing operations using local and global smart factors. The Ants Colony Algorithm is a multi-factor solution for optimization issues. This solution has models based on the ants’ collective intelligence and has attracted some users in computer networks through converting to an efficient technology. Although the Ant is a simple insect, but a colony of them are able to perform useful tasks such as finding the shortest path to the food source and to share this information with other ants through leaving back a chemical material called pheromone. This algorithm consists of three stages. The first phase is clustering nodes of the network to smaller colonies. This phase is conducted by using learning automata network in accordance with the need of the network; For example, putting nodes in one cluster which will have more close relations in near future. The second phase is finding the routes of the network by ants, and the third phase is sending network