In order to further improve the shortcomings that the basic salp swarm algorithm is easy to fall into the local optimum, the optimization accuracy is sometimes not high, and the solution results are not stable, a global search-oriented adaptive leader salp swarm algorithm is proposed. The location of the last generation salp swarm group is introduced into the leader position update formula, which enhances the sufficiency of global search and effectively avoids the algorithm falling into local extremum. Then the inertia weight is added to the leader position update formula, and the leader-follower adaptive adjustment strategy is introduced to the choice of global and local search, so that the algorithm has a large number of leaders in the early iteration and is greatly influenced by the global optimal solution, it can quickly converge to the global optimal region with a larger global search step. At the end of the iteration, the leader's stride is small and the number of followers is large, so the algorithm can be mined deeply near the optimal solution to improve the convergence accuracy. Then the algorithm flow is given and the time complexity is analyzed theoretically. Finally, through the simulation experiment of function optimization of 5 representative comparison algorithms on multiple dimensions of 10 different feature benchmark functions, the test results show that the optimization accuracy and stability of the improved algorithm are significantly improved.