Aiming at the problems that the standard hippopotamus optimization algorithm (HO) has slow convergence speed, easy to fall into local optimum and imbalance between search and development, this study proposes the multi-strategy cooperative hippopotamus optimization algorithm (MSCHO). Circle mapping is introduced in the initialization stage to enhance the diversity of the initialization population and reduce the blind area of initialization; secondly, the mirror-pole random wandering strategy is introduced in the HO position updating stage to enhance the possibility of finding the optimal solution; finally, nonlinear adaptive Cosine adaptive t-distribution variation is used in the HO exploration stage to improve the convergence speed and stability of the algorithm. In order to verify the improvement effect and performance of the algorithm, five swarm intelligent optimization algorithms are selected to simulate eight classical test functions and the experimental results show that compared with other algorithms, the improved MSCHO has more excellent optimization ability and convergence speed.