Abstract: In view of the disadvantages of slow convergence， low solution accuracy and tendencies to fall into local optimal solutions in the optimization process， an improved adaptive mutation strategy of salp swarm algorithm based on Cauchy and Gaussian mixture mutation was proposed. The algorithm tend not to fall into local optimal solution by selecting the first S individuals with the best fitness value for adaptive variations， and the local search capability and convergence speed of the algorithm is improved by dynamically adjusting the size variation of the parameters through the Cauchy and Gaussian variants. Ten test functions were selected to test and compare the salp swarm algorithm and the improved adaptive mutation salp swarm algorithm respectively. Numerical analysis showed that the improved salp swarm algorithm has fast convergence speed， strong search ability and high accuracy. The improved algorithm was applied to the optimization design of the main beam structure of the beam lifter. Under the condition of meeting the design requirements of strength， stiffness and stability， etc.， the cross-sectional area of the main beam is reduced by 13.58 %， and the weight reduction effect is significant， indicating that the algorithm has good engineering application value.

Key words: salp swarm algorithm; adaptive mutation strategy; Cauchy mutation; Gaussian mutation; girder hoist; weight reduction