Planning a reasonable communication routing is key to improving the transmission efficiency of the sea buoy network. The ant colony optimization (ACO) is an effective heuristic optimization algorithm and has been widely used to solve the route planning problem in communication networks. As the iterations proceed of the traditional ACO, individuals tend to become increasingly similar, thereby increasing the algorithm's susceptibility to falling into local optima. In this paper, we propose an improved ant colony optimization (IACO) to solve the communication routing problem of the sea buoy network. Specifically, we introduce two optimization operators to exchange the routing information between excellent individuals, improving the local search capability of the IACO. In addition, to adaptively balance the exploration and exploitation of the population, a dynamic individual selection strategy based on Hamming distance is proposed. This strategy selects individuals with different diversity and fitness to conduct the designed optimization operators at different stages of the optimization process. Finally, simulation results show that the IACO can plane reasonable routing schemes for the buoy network routing problem.