Evolving the cooperative behavior in Iterated N-Players Prisoners’ Dilemma (INPPD) is studied over several evolutionary models. These models presented solutions for evolving cooperative behavior among INPPD players. Studying existing models revealed that when the number of the players’ increases, the models lose their capabilities in maintaining stable levels of cooperation between the players. In this paper, we present an evolutionary model for enhancing the cooperation levels in large population of INPPD players. The model focuses on optimizing the communication topology of INPPD, as well as building a knowledge base to support players’ future decisions based on the evolved knowledge gained from historical actions taken by different players. The presented communication topology along with the knowledge base present considerable support for the evolutionary Particle Swarm Optimization (PSO) algorithm to evolve the players’ strategies. The results showed that the model could increase the cooperative rate among INPPD player and allow players to achieve higher payoffs against benchmark strategies