Abstract: Aiming at the shortcomings of the Marine Predators Algorithm （MPA） such as slow convergence speed， low solution accuracy and easy to fall into local optimum， a Marine Predators Algorithm based on adaptive rotation learning and crisis awareness strategy （ARCMPA） is proposed. First of all， in view of the slow convergence speed and low convergence precision of the MPA， a crisis awareness strategy is introduced to improve the ability of the algorithm to explore the solution space， strengthen the lack of early development capabilities of the algorithm， speed up the convergence speed of the early algorithm， and improve the quality of the algorithm solution. Secondly， an adaptive rotation learning mechanism is introduced to make the position distribution of the entire population more uniform， effectively enhancing the diversity of the population during the iteration of the algorithm， and preventing the algorithm from falling into local optimum after accelerating the convergence speed in the early stage. Through the introduction of two strategies， the overall performance of the algorithm is effectively enhanced. In this paper， 10 benchmark functions are selected and compared with other metaheuristic algorithms. Experimental results show that the above improvements help to improve the overall performance of the algorithm.

Key words:  , metaheuristics, marine predators, adaptive rotation learning, crisis awareness strategies