Abstract: Because the basic ocean predator algorithm has shortcomings such as low solution accuracy and poor stability at runtime, this paper proposes an elite reverse learning golden sine ocean predator algorithm. The elite reverse learning mechanism is added to improve the population quality of marine predators, and the comprehensive exploration scope of the algorithm is effectively expanded. The golden sine strategy is added to improve the way of hunting their prey of marine predators and reduce the search space of marine predators, and the performance of local development of the algorithm is effectively enhanced. The effectiveness of the improved strategy is evaluated by solving 12 benchmark functions and 12 CEC2017 functions. The solution test results show that the two strategies have an excellent effect on improving the performance of the ocean predator algorithm.  

Key words: elite opposition-based learning, golden sine, marine predators algorithm