In order to improve the rate of convergence and obtain high optimization precision of cuckoo search,a hybrid optimization algorithm for solving complicated optimization problems was proposed.The proposed algorithm combines model-based cross-entropy method with population-based cuckoo search.The hybrid algorithm not only improves the rate of convergence but also enhances the global search ability by adopting the co-evolution strategy.Simulated experiments were conducted on classical benchmarks and PID controller tuning problem.The results show that the proposed algorithm possesses more powerful global search capacity,higher optimization precision and robustness,and is feasible and effective for solving complicated optimization problems.