We present an algorithm which integrates flow control and dynamic load balancing in Time Warp. The algorithm is intended for use in a distributed memory environment. Our flow control algorithm makes use of stochastic learning automata and is similar to the leaky-bucket flow control algorithm used in computer networks. It regulates the flow of messages between processors continously throughout the course of the simulation, while the dynamic load balancing algorithm is invoked only when a load imbalance is detected. We compare the perfomance of the flow control algorithm, the dynamic load balancing algorithm and the integrated algorithm with that of a simulation without these controls. We simulated large shuffle ring networks with and without hot spots and a PCS network on an SGI Origin 2000 system. Our results indicate that the flow control scheme alone succeeds in greatly reducing the number and length of rollbacks as well as the number of anti-messages, thereby increasing the number of non-rolledback messages processed per second. It results in a large reduction in the amount of memory used and outperforms the dynamic load balancing algorithm for these measures. The integrated scheme produces even better results for all of these measures and results in reduced execution times.