Abstract: Aiming at the shortcomings of the butterfly optimization algorithm, such as slow convergence speed, poor searching precision and easy to fall into local optimality. A butterfly optimization algorithm based on convergence factor and salp swarm is proposed, inspired by grey wolf algorithm and salp swarm algorithm, the convergence factor is integrated into global position and local position update respectively, the optimization precision of algorithm is improved; combined with the salp swarm leadership mechanism, the global search and local exploration capabilities of the algorithm are balanced. By testing 17 benchmark functions, all the experimental results show that the algorithm using the comprehensive improved strategy has some advantages in terms of convergence speed, optimization accuracy and robustness.