Resource discovery in the peer to peer networks using an inverted ant colony optimization algorithm

In recent years, the attractiveness of Peer-to-Peer (P2P) networks has been grown rapidly due to the easiness of use. The P2P system is a decentralized relationship model in which every party has the analogous abilities and either party can start a relationship session. In these networks, due to the high number of users, the resource discovery process becomes one of the important parts of the P2P networks. But, in many previously proposed methods, there is a common problem that is called load balancing. If the balance of workload is inefficient, it reduces the resource utilization. Therefore, in this article, we propose the Inverted Ant Colony Optimization (IACO) algorithm, a variety of the basic Ant Colony Optimization (ACO) algorithm, to improve load balancing among the peers. In the proposed method, the effect of pheromone on the selected paths by ants is inverted. In this approach, ants start to traverse the graph from the requester peer and each ant chooses the best peer for moving. Then, requirements and pheromone amount are updated. Finally, we simulate the method and evaluate its performance in comparison to the ACO algorithm in different terms. The obtained results show that the performance of the IACO is better than the ACO algorithm in terms of load balancing, waiting time and resource utilization.