Abstract: In order to increase the speed and performance of parameter decision in cognitive radio system,a cognitive radio decision engine (HPSO-BLDE) based on hybrid particle swarm optimization and learning differential evolution algorithm was proposed.First,the adaptive mutation mechanism is introduced into the learning differential evolution algorithm,so that each chromosome adaptively varies with individual fitness and average fitness to improve its local optimization capability.Then,the learning factor of particle swarm optimization algorithm is modified and the perturbation is added to prevent the premature.The more appropriate transform function is selected to convert the forward and backward velocity to the same probability to update the particle position and improve the precision of the optimal solution,thus improving the global optimization solution.Finally,the improved binary particle swarm optimization (IBPSO) and the improved binary differential evolution algorithm (IBLDE) are run in parallel in the cognitive engine model,and the best individual information of the two algorithms is fused after a fixed number of iterations to obtain the HPSO-BLDE algorithm.The populations of IBPSO algorithm and IBLDE algorithm have the both advantages,thus the optimal solution accuracy and convergence speed of the HPSO-BLDE algorithm are enhanced.Parameter decision simulation results of multi-carrier communication system shows that the IBPSO algorithm,IBLDE algorithm and HPSO-BLDE algorithm have better performance than hilling genetic algorithm (HGA),binary quantum particle swarm algorithm (BQPSO) and binary learningdifferential evolution algorithm (BLDE),and HPSO-BLDE algorithm has the best performance among these algorithms.

Key words: Cognitive decision engine, Cognitive radio, Differential evolution, Particle swarm optimization, Reconfiguration