融合学习差分进化和粒子群优化算法的认知决策引擎  

Cognitive Decision Engine of Hybrid Learning Differential Evolution and Particle Swarm Optimization

作　　者：张煜培 赵知劲[1,2] 郑仕链[2] ZHANG Yu-pei;ZHAO Zhi-jin;ZHENG Shi-lian(Zhejiang Provincial Key Lab of Data Storage and Transmission Technology of Hangzhou Dianzi University,Hangzhou 310018,China;State Key Lab of Information Control Technology in Communication System,No.36 Research Institute of China Electronic Technology Corporation,Jiaxing,Zhejiang 314001,China)

机构地区：[1]杭州电子科技大学浙江省数据存储传输及应用技术研究重点实验室,杭州310018 [2]中国电子科技集团第36研究所通信系统信息控制技术国家级重点实验室,浙江嘉兴314001

出　　处：《计算机科学》2019年第6期95-101,共7页Computer Science

基　　金：“十二五”国防预研项目基金(41001010401)资助

摘　　要：为了提高认知无线电系统的参数决策速度和性能,提出一种融合粒子群和学习差分进化算法的认知无线电决策引擎(HPSO-BLDE)。首先,对学习差分进化算法引入自适应变异机制,使得每条染色体随个体适应度和平均适应度进行自适应变异,提高其局部寻优能力。然后,改进粒子群算法的学习因子,并加入扰动项,防止算法早熟;选用更合适的变换函数,将正反向速度转换为相同概率更新粒子位置,提高最优解的精度,从而提高粒子群算法的全局寻优能力。最后,在认知引擎模型中并行地运行改进的粒子群算法(IBPSO)和差分进化算法(IBLDE),每隔固定的迭代次数后,融合两种算法的最优个体信息,得到HPSO-BLDE算法,使IBPSO算法和IBLDE算法的种群兼具二者的优点,从而提高了最优解的求解精度并加快了收敛速度。多载波通信系统的参数决策仿真结果表明,IBPSO算法、IBLDE算法和HPSO-BLDE算法的性能优于已有的爬山遗传(HGA)算法、量子粒子群算法(BQPSO)和二进制学习差分进化算法(BLDE),其中HPSO-BLDE算法的性能最优。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 learning differential evolution algorithm(BLDE),and HPSO-BLDE algorithm has the best performance among these algorithms.

关 键 词：认知无线电 认知决策引擎 重配置 差分进化 粒子群优化 

分 类 号：TN929.5[电子电信—通信与信息系统]