A Multi-Disturbance Marine Predator Algorithm Based on Oppositional Learning and Compound Mutation

Abstract

Marine Predator Algorithm (MPA) is a meta-heuristic algorithm based on the foraging behavior of marine animals. It has the advantages of few parameters, simple setup, easy implementation, accurate calculation, and easy application. However, compared with other meta-heuristic algorithms, this algorithm has some problems, such as a lack of transition between exploitation and exploration and unsatisfactory global optimization performance. 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.