El Algoritmo del Depredador Marino (MPA) es un algoritmo metaheurístico basado en el comportamiento de forrajeo de los animales marinos. Tiene ventajas como pocos parámetros, configuración simple, implementación sencilla, cálculos precisos y fácil aplicación. Sin embargo, en comparación con otros algoritmos metaheurísticos, este algoritmo presenta algunos problemas, como la falta de transición entre explotación y exploración y un rendimiento de optimización global insatisfactorio. Aiming at the shortage of MPA, this paper proposes a multi-disturbance Marine Predator Algorithm based on oppositional learning and compound mutation (mMPA-OC). Firstly, the optimal value selection process is improved by using Opposition-Based Learning mechanism and enhance MPA"s exploration ability. Secondly, the combined mutation strategy was used to improve the predator position updating mechanism and improve the MPA"s global search ability. Finally, the disturbances factors are improved to multiple disturbances factors, so that the MPA could maintain the population diversity. In order to verify the performance of the mMPA-OC, experiments are conducted to compare mMPA-OC with seven meta-heuristic algorithms, including MPA on different dimensions of the CEC-2017 benchmark function, complex CEC-2019 benchmark function, and engineering optimization problems. Experiments have shown that the mMPA-OC is more efficient than other meta-heuristic algorithms.