An improved hybrid mayfly algorithm for global optimization

As the complexity of optimization problems increases, metaheuristic algorithms play an important role in dealing with complex computational problems and try to find the best solution from all feasible solutions to the problem. The mayfly algorithm is a novel metaheuristic algorithm based on the social behavior of biological groups. The algorithm achieves global and local search by simulating the flight behavior and mating process of mayflies to obtain global optimal solution. However, the traditional mayfly algorithm has problems such as low convergence accuracy and poor stability and is prone to becoming trapped in local optimality. Aiming at the problem of the mayfly algorithm, an improved mayfly algorithm combined with the gray wolf optimization algorithm (MA-GWO) is proposed. In the mayfly algorithm, the Lévy flight strategy and the hunting mechanism of the gray wolf optimization algorithm are introduced to achieve complementary advantages. To verify the superiority of the proposed algorithm, 19 classical benchmark functions, CEC-C06 2019 test functions and 5 engineering design problems are compared with various advanced metaheuristic algorithms. The experimental data show that the MA-GWO algorithm has significant enhancements over the traditional mayfly algorithm. In several test cases, especially for high-dimensional optimization problems, the MA-GWO algorithm is far superior to other metaheuristic algorithms, has better convergence and stability, and is an effective and feasible algorithm.