Abstract: In view of the problems of the basic butterfly optimization algorithm, such as slow convergence speed, low solution accuracy and being prone to local optimum, an adaptive mutation butterfly optimization algorithm is proposed. Firstly, improved tent map barycenter reverse learning is used to the population to gain a better initial solution. Secondly, the nonlinear inertial weight is introduced in the location update to balance the global search and local search capabilities of the algorithm. Finally, the variance of population fitness and the size of the current optimal solution determine whether to carry out Gaussian mutation quadratic optimization for the current optimal solution and the worst solution, in order to enhance the ability of the algorithm to jump out of the local optimum. The multi-dimensional simulation results of 12 benchmark functions show that the proposed algorithm is be obviously better than other alignment algorithms in convergence speed, solution accuracy and optimization stability.

Key words: butterfly optimization algorithm, tent map, centroid opposition-based learning, nonlinear inertial weight, Gaussian mutation