Abstract: In internet computing, swarm intelligence has shown growing interest in study dynamic optimization problems. Many approaches are developed for SI to enhance the diversity of the population and improve the performance of the algorithm for DOPs. Out of these approaches, immigrants schemes are found useful for SIs in DOPs. In this study, random, elitism based and hybrid based immigrants schemes are applied to Bacteria Foraging Optimization Algorithm (BFOA) for the Dynamic Shortest Path Routing Problem (DSPRP). The simulation results show that random immigrants are useful for BFOA in quick changing environments, whereas elitism-based immigrants are useful for BFOA in gradually ever changing environments. The BFOA algorithm with a hybrid based immigrants scheme combines the merits of the random and elitism immigrants schemes. Moreover, the simulation results show that the proposed algorithms outperform in almost all dynamic test cases and immigrant based BFOA schemes enhance the performance efficiently in DSPRP.