Abstract: One improved Quantum-behaved particle swarm optimization for intelligent test paper generation was put forward．First of all,inertia weight is expressed as functions of particle evolution velocity and particle aggregation by defining them．Secondly,slowly varying function is introduced into the traditional position updating formula to effectively overcome the problem of getting into the local optimal solution．Finally,mathematical modeling is set for test paper genera-tion problems based on item response theory．Simulation results show that,comparing with the standard particle swarm optimization algorithm and quantum particle swarm algorithm,the proposed algorithm is of better performance in the success rate and efficiency of test paper generation．

Key words: Quantum-behaved particle swarm optimization,Self-adaptive,Inertia weight,Slowly varying function,Item response theory(IRT),Autogenerating test paper