中文摘要：

In order to effectively solve combinatorial optimization problems,a membrane-inspired quantum bee colony optimization(MQBCO)is proposed for scientific computing and engineering applications.The proposed MQBCO algorithm applies the membrane computing theory to quantum bee colony optimization(QBCO),which is an effective discrete optimization algorithm.The global convergence performance of MQBCO is proved by Markov theory,and the validity of MQBCO is verified by testing the classical benchmark functions.Then the proposed MQBCO algorithm is used to solve decision engine problems of cognitive radio system.By hybridizing the QBCO and membrane computing theory,the quantum state and observation state of the quantum bees can be well evolved within the membrane structure.Simulation results for cognitive radio system show that the proposed decision engine method is superior to the traditional intelligent decision engine algorithms in terms of convergence,precision and stability.Simulation experiments under different communication scenarios illustrate that the balance between three objective functions and the adapted parameter configuration is consistent with the weights of three normalized objective functions.

英文摘要：

In order to effectively solve combinatorial optimization problems, a membrane-inspired quantum bee colony optimization （MQBCO） is proposed for scientific computing and engineering applications. The proposed MQBCO algorithm applies the membrane computing theory to quantum bee colony optimization （QBCO）, which is an effective discrete optimization algorithm. The global convergence performance of MQBCO is proved by Markov theory, and the validity of MQBCO is verified by testing the classical benchmark functions. Then the proposed MQBCO algorithm is used to solve decision engine problems of cognitive radio system. By hybridizing the QBCO and membrane computing theory, the quantum state and observation state of the quantum bees can be well evolved within the membrane structure. Simulation results for cognitive radio system show that the proposed decision engine method is superior to the traditional intelligent decision engine algorithms in terms of convergence, precision and stability. Simulation experiments under different communication scenarios illustrate that the balance between three objective functions and the adapted parameter configuration is consistent with the weights of three normalized objective functions.