The grey wolf optimization algorithm (GWO) has some demerits on solving more complex nonlinear optimization problems

such as relapsing into local extremum

slow convergence velocity and low convergence precision in the late evolutionary. this paper proposed a kind GWO algorithm based on levy flight (LGWO) via introducing of levy flight that is random walk. When updating the wolfs position

levy flight was used to conduct a global search on the wolf to prevent the wolves from losing diversity and easily falling into a local optimal solution. Through simulation

compared with the classic cuckoo search algorithm、particle swarm optimization algorithm and other group intelligence algorithms

the results show that the grey wolf optimization algorithm based on levy flight can effectively improve the accuracy of the solution and convergence speed

and the optimization effect is better.