Exploring an unknown environment using team of autonomous mobile robots is an important task in many real world applications. Many existing map exploration algorithms are based on frontier, which is the boundary between unexplored space and known open space. In the context of multiple robots, the main problem of frontier-based algorithm is to choose appropriate target points for the individual robots so that they can efficiently explore the different part of the common area. This project proposes a novel distributed frontier-based map exploration algorithm using Particle Swarm Optimization model for robot coordination. In this algorithm, each system will considered to be a master or slave in which master can control and communicate with slave as well as slave to slave control and communication is possible. The robot keeps moving to the nearby frontier to reduce the size of the unknown region, and is navigated towards frontier far away based on the PSO model after exploring the local area. The exploration is completed when there are no frontier cells on the map. Our algorithm has been implemented and tested real world experiment. The result shows that our method has a good scalability and efficiency.