A Chicken Swarm Optimization based on Hooke-Jeeves(HJCSO) was proposed to solve the defect that traditional Chicken Swarm Optimization(CSO) is easy to fall into local optimum. In the iteration of the algorithm

the examination and judgment of the optimal solution was added. When it was found that the solution remained unchanged many times

the algorithm was considered to be locally optimal. Hooke-Jeeves algorithm was introduced to help the algorithm to jump out of the local optimum. The differential results between the global optimal solution and the global suboptimal solution was used to drag the optimal solution in each iteration for neighborhood search. When it comes to the convergence precision of the algorithm is guaranteed

the convergence speed is improved. The results on five typical standard test functions show that the improved CSO not only improves the global search ability

but also the search efficiency

search accuracy and convergence rate are better than the traditional CSO. Especially in dealing with high-dimensional function problems

the improved HJCSO shows a strong advantage.