Abstract: For complex functions with high dimensions,canonical optimization methods are easy to be trapped in local minima and simple random search methods are slow on convergence.This paper proposes an effective hybrid optimization which combines particle swarm optimization with simple method,and it adopts quick and broad searching of the former,and quick convergence of the latter simultaneously.Simulation results on benchmark complex functions with high dimensions show that the hybrid algorithm is effective,efficient and fairly robust to initial conditions.The performance of the hybrid algorithm excels those of single optimization methods.