Chaos quantum clonal algorithm for decision engine of cognitive wireless network

Abstract

By analyzing engine decision of cognitive wireless network, the mathematical model of engine decision is given, and then it is converted into a multi-objective optimization problem. A Chaos quantum clonal algorithm is proposed to solve the problem, and the algorithm convergent with probability 1 is proved, in which the quantum coding and logistic mapping are used to initialize the population. A quantum mutation scheme is designed with chaotic disturbances. Finally, the simulation experiments are done to test the algorithm under a multi-carrier system. The results show that compared with QGA-CE (quantum genetic algorithm based cognitive engine), this algorithm has a good convergence and an objective function value. It can adapt the parameter configuration and meet the real-time requirement for cognitive engine.