Because the Particle Swarm Optimization (PSO) algorithm is easily trapped into local optimal solutions when solving the bi-level programming problems, an Improved hybrid Cuckoo Search-based Quantum-behaved Particle Swarm Optimization (ICSQPSO) algorithm based on Simulated Annealing (SA) Metropolis criterion was proposed. Firstly, the Metropolis criterion of SA algorithm was introduced into the hybrid algorithm to enhance the global optimization ability by accepting good solutions as well as bad solutions with a probability during solving process. Secondly, a Lévy flight with dynamic step size was designed for cuckoo search algorithm in order to maintain the high diversity of particle swarm during optimization, so as to guarantee search range. Finally, the preference random walk mechanism in the cuckoo algorithm was used to help the particles jump out of local optimal solutions. The numerical results of 13 bi-level programming cases including nonlinear ones, fractional ones, and those with multiple lower levels show that the objective functions optimal values of 12 cases obtained by ICSQPSO algorithm are significantly better than those of the algorithms for comparison in literatures, only the result of 1 case is slightly worse, and the results of half of the 13 cases are 50% better than those of the algorithms to be compared. Therefore, the ICSQPSO algorithm is superior to the algorithms to be compared on the optimization ability for bi-level programming.